Systems and methods for sample preparation, data generation, and protein corona analysis

ABSTRACT

Systems and methods for automated sample preparation and processing of protein corona are described herein, as well as its application in the discovery of advanced diagnostic tools as well as therapeutic agents.

CROSS-REFERENCE

The present application is a continuation of U.S. Non-Provisionalapplication Ser. No. 17/216,523, filed Mar. 29, 2021, which is acontinuation of International Application No. PCT/US2020/044908, filedAug. 4, 2020, which claims the benefit of U.S. Provisional PatentApplication No. 62/883,107, filed Aug. 5, 2019, each of which is hereinincorporated by reference in its entirely.

BACKGROUND

Broad scale implementation of proteomic information in science andmedicine has lagged behind genomics in large part because ofcomplexities inherent in protein molecules themselves, necessitatingcomplex workflows that limit the scalability of such analyses. Disclosedherein are systems, methods and kits for rapid and automated samplepreparation, processing of proteomic data and the identification of keybiomarkers associated with diseased states.

SUMMARY

The present disclosure provides automated systems, methods and kits forprotein corona preparation and analysis. In some aspects, the presentdisclosure provides an automated apparatus for generating a subset ofbiomolecules from a complex biological sample, the automated apparatuscomprising: (i) a substrate comprising a plurality of partitions,wherein the plurality of partitions comprises a plurality of particles;(ii) a sample storage unit comprising the complex biological sample; and(iii) a loading unit that is movable at least across the substrate,wherein the loading unit transfers one or more volumes of the complexbiological sample in the sample storage unit to the plurality ofpartitions on the substrate, thereby contacting the plurality ofparticles in the plurality of partitions with biomolecules of thecomplex biological sample to form biomolecule coronas, therebygenerating the subset of biomolecules of the complex biological sample,and wherein a dynamic range of the subset of biomolecules is compressedrelative to a dynamic range of biomolecules present in the complexbiological sample. In some embodiments, the substrate is a multi-wellplate. In some embodiments, the subset of biomolecules comprises atleast 20% to at least 60% of the types of biomolecules from the complexbiological sample within a 6 order of magnitude concentration range. Insome embodiments, the subset of biomolecules comprises at least 20% toat least 60% of the types of proteins from the complex biological samplewithin a 6 order of magnitude concentration range. In some embodiments,the automated apparatus generates the subset of biomolecules from acomplex biological sample in less than 7 hours.

In some embodiments, the automated apparatus comprises an incubationelement that agitates or heats volumes of the plurality of particleswithin volumes of the complex biological sample in the plurality ofpartitions. In some embodiments, the incubation element is configured toshake, mix, stir, spin, vibrate, be static, or any combination thereof.In some embodiments, the wherein the incubation element is configured toheat and/or incubate the substrate to a temperature between about 20° C.and about 100° C.

In some embodiments, the plurality of partitions is at least partiallycovered or sealed. In some embodiments, a partition from among theplurality of partitions is covered or sealed. In some embodiments, theautomated apparatus comprises the ability to add or remove a lid on thesubstrate, wherein the lid covers at least one of the partitions fromamong the plurality of partitions.

In some embodiments, the automated apparatus comprises a unit comprisinga resuspension solution. In some embodiments, the resuspension solutioncomprises Tris EDTA 150 mM KCl 0.05% CHAPS buffer. In some embodiments,the resuspension solution comprises 10 mM Tris HCl pH 7.4, 1 mM EDTA.

In some embodiments, the apparatus comprises a unit comprising adenaturing solution. In some embodiments, the denaturing solutioncomprises a protease. In some embodiments, the denaturing solutioncomprises a reductant, a methylating agent, guanidine, urea, sodiumdeoxycholate, acetonitrile, or any combination thereof. In someembodiments, the denaturing solution generates an average peptidefragment with a mass of less than 4600 Daltons.

In some embodiments, the loading unit comprises a plurality of pipettes.In some embodiments, the loading unit is configured to dispense 10 uL to400 uL of a solution into one or more partitions of the plurality ofpartitions. In some embodiments, the loading unit is configured todispense 5 uL to 150 uL of a solution into one or more partitions of theplurality of partitions. In some embodiments, the loading unit isconfigured to dispense 35 uL to 80 uL of a solution into one or morepartitions of the plurality of partitions. In some embodiments, thesolution is selected from the group consisting of a wash solution, theresuspension solution, the denaturing solution, a buffer and a reagent.In some embodiments, the loading unit is configured to dispense 10 uL to400 uL of the complex biological sample into one or more partitions ofthe plurality of partitions. In some embodiments, the loading unit isconfigured to dispense 5 uL to 150 uL of the complex biological sampleinto one or more partitions of the plurality of partitions. In someembodiments, the loading unit is configured to dispense 35 uL to 80 uLof the complex biological sample into one or more partitions of theplurality of partitions.

In some embodiments, the complex biological sample comprises a biofluidfrom a subject. In some embodiments, the complex biological samplecomprises plasma, serum, urine, cerebrospinal fluid, synovial fluid,tears, saliva, whole blood, milk, nipple aspirate, ductal lavage,vaginal fluid, nasal fluid, ear fluid, gastric fluid, pancreatic fluid,trabecular fluid, lung lavage, sweat, crevicular fluid, semen, prostaticfluid, sputum, fecal matter, bronchial lavage, fluid from swabbings,bronchial aspirants, fluidized solids, fine needle aspiration samples,tissue homogenates, lymphatic fluid. cell culture samples, or anycombination thereof.

In some embodiments, the automated apparatus further comprises a magnet.In some embodiments, one or more particles of the plurality of particlesis a magnetic particle, and the substrate and the magnet are inproximity such that the one or more magnetic particles are immobilizedon the substrate

In some embodiments, the automated apparatus further comprises ahousing, the substrate and the loading unit are located in the housing,and the housing is at least partially enclosed.

In some embodiments, the compressed dynamic range comprises an increasein the number of types of biomolecules whose concentrations are within 6orders of magnitude of the most abundant biomolecule in the sample. Insome embodiments, the compressed dynamic range comprises an increase inthe number of types of biomolecules whose concentrations are within 5orders of magnitude of the most abundant biomolecule in the sample. Insome embodiments, the compressed dynamic range comprises an increase inthe number of types of biomolecules whose concentrations are within 4orders of magnitude of the most abundant biomolecule in the sample. Insome embodiments, the compressed dynamic range comprises an increase inthe number of types of proteins whose concentrations are within 6 ordersof magnitude of the most abundant protein in the sample. In someembodiments, the increase in the number of types of biomolecules whoseconcentrations are within 6 orders of magnitude of the most concentratedbiomolecule in the sample is at least 25%, 50%, 100%, 200%, 300%, 500%,or 1000%. In some embodiments, the compressed dynamic range comprises anincrease in the number of types of proteins whose concentrations arewithin 6 orders of magnitude of the most abundant protein in the sample.In some embodiments, the increase in the number of types of proteinswhose concentrations are within 6 orders of magnitude of the mostabundant protein in the sample is at least 25%, 50%, 100%, 200%, 300%,500%, or 1000%.

In some embodiments, the dynamic range of the biomolecules of thebiomolecule coronas is a first ratio of a top decile of biomolecules toa bottom decile of biomolecules in the plurality of biomolecule coronas.In some embodiments, the dynamic range of the biomolecules of thebiomolecule coronas is a first ratio comprising a span of theinterquartile range of biomolecules in the plurality biomoleculecoronas.

In some embodiments, the generating enriches low abundance biomoleculesfrom the complex biological sample. In some embodiments, the lowabundance biomolecules are biomolecules at concentrations of 10 ng/mL orless in the complex biological sample. In some embodiments, the subsetof biomolecules from the complex biological sample comprises proteins.

In some embodiments, changes of at most 10 mg/mL in the lipidconcentration of the complex biological sample result in changes of lessthan 10%, 5%, 2%, or 1% in the composition of the proteins in the subsetof biomolecules generated from the complex biological sample.

In some embodiments, at least two particles from among the plurality ofparticles differ in at least one physicochemical property. In someembodiments, the at least one physicochemical property is selected fromthe group consisting of: composition, size, surface charge,hydrophobicity, hydrophilicity, surface functionality, surfacetopography, surface curvature, porosity, core material, shell material,shape, and any combination thereof. In some embodiments, the surfacefunctionality comprises aminopropyl functionalization, aminefunctionalization, boronic acid functionalization, carboxylic acidfunctionalization, methyl functionalization, N-succinimidyl esterfunctionalization, PEG functionalization, streptavidinfunctionalization, methyl ether functionalization,triethoxylpropylaminosilane functionalization, thiol functionalization,PCP functionalization, citrate functionalization, lipoic acidfunctionalization, BPEI functionalization. In some embodiments, aparticle from among the plurality of particles is selected from thegroup consisting of: micelles, liposomes, iron oxide particles, silverparticles, gold particles, palladium particles, quantum dots, platinumparticles, titanium particles, silica particles, metal or inorganicoxide particles, synthetic polymer particles, copolymer particles,terpolymer particles, polymeric particles with metal cores, polymericparticles with metal oxide cores, polystyrene sulfonate particles,polyethylene oxide particles, polyoxyethylene glycol particles,polyethylene imine particles, polylactic acid particles,polycaprolactone particles, polyglycolic acid particles,poly(lactide-co-glycolide polymer particles, cellulose ether polymerparticles, polyvinylpyrrolidone particles, polyvinyl acetate particles,polyvinylpyrrolidone-vinyl acetate copolymer particles, polyvinylalcohol particles, acrylate particles, polyacrylic acid particles,crotonic acid copolymer particles, polyethlene phosphonate particles,polyalkylene particles, carboxy vinyl polymer particles, sodium alginateparticles, carrageenan particles, xanthan gum particles, gum acaciaparticles, Arabic gum particles, guar gum particles, pullulan particles,agar particles, chitin particles, chitosan particles, pectin particles,karaya tum particles, locust bean gum particles, maltodextrin particles,amylose particles, corn starch particles, potato starch particles, ricestarch particles, tapioca starch particles, pea starch particles, sweetpotato starch particles, barley starch particles, wheat starchparticles, hydroxypropylated high amylose starch particles, dextrinparticles, levan particles, elsinan particles, gluten particles,collagen particles, whey protein isolate particles, casein particles,milk protein particles, soy protein particles, keratin particles,polyethylene particles, polycarbonate particles, polyanhydrideparticles, polyhydroxyacid particles, polypropylfumerate particles,polycaprolactone particles, polyamine particles, polyacetal particles,polyether particles, polyester particles, poly(orthoester) particles,polycyanoacrylate particles, polyurethane particles, polyphosphazeneparticles, polyacrylate particles, polymethacrylate particles,polycyanoacrylate particles, polyurea particles, polyamine particles,polystyrene particles, poly(lysine) particles, chitosan particles,dextran particles, poly(acrylamide) particles, derivatizedpoly(acrylamide) particles, gelatin particles, starch particles,chitosan particles, dextran particles, gelatin particles, starchparticles, poly-β-amino-ester particles, poly(amido amine) particles,poly lactic-co-glycolic acid particles, polyanhydride particles,bioreducible polymer particles, and 2-(3-aminopropylamino)ethanolparticles, and any combination thereof. In some embodiments, one or moreparticles of the plurality of particles adsorbs at least 100 types ofproteins upon contacting the complex biological sample. In someembodiments, the plurality of particles comprises at least 2 distinctparticle types, at least 3 distinct particle types, at least 4 distinctparticle types, at least 5 distinct particle types, at least 6 distinctparticle types, at least 7 distinct particle types, at least 8 distinctparticle types, at least 9 distinct particle types, at least 10 distinctparticle types, at least 11 distinct particle types, at least 12distinct particle types, at least 13 distinct particle types, at least14 distinct particle types, at least 15 distinct particle types, atleast 20 distinct particle types, at least 25 particle types, or atleast 30 distinct particle types.

In some embodiments, biomolecules of the biomolecule coronas comprise anumber of protein groups. In some embodiments, the number of proteingroups comprises from 1 to 20,000 protein groups. In some embodiments,the number of protein groups comprises from 100 to 10,000 proteingroups. In some embodiments, the number of protein groups comprises from100 to 5000 protein groups. In some embodiments, the number of proteinsgroups comprises from 300 to 2,200 protein groups. In some embodiments,the number of proteins groups comprises from 1,200 to 2,200 proteingroups.

In some embodiments, at least two partitions of the plurality ofpartitions comprise different buffers. In some embodiments, thedifferent buffers differ in pH, salinity, osmolarity, viscosity,dielectric constant, or any combination thereof. In some embodiments, atleast two partitions of the plurality of partitions comprise differentratios of buffer and the complex biological sample. In some embodiments,one or more partitions of the plurality of partitions comprises 1 pM to100 nM nanoparticles. In some embodiments, at least two partitions ofthe plurality of partitions comprise different concentrations ofnanoparticles.

In some embodiments, the automated apparatus further comprises apurification unit. In some embodiments, the purification unit comprisesa solid phase extraction (SPE) plate.

Various aspects of the present disclosure provide an automated systemcomprising: (i) an automated apparatus configured to isolate the subsetof biomolecules from the biological sample; (ii) a mass spectrometerconfigured to receive the subset of biomolecules and to generate datacomprising mass spectrometric or tandem mass spectrometric signals; and(iii) a computer comprising one or more computer processors and acomputer readable medium comprising machine-executable code that, uponexecution by the one or more computer processors, implements a methodcomprising: generating a biomolecule fingerprint and assigning abiological state based on the biomolecule fingerprint.

In some embodiments, the biomolecule fingerprint comprises a pluralityof distinct biomolecule corona signatures. In some embodiments, thebiomolecule fingerprint comprises at least 5, 10, 20, 40, or 80, 150 or200 distinct biomolecule corona signatures. In some embodiments, thecomputer is configured to process the data comprising the intensity,APEX, spectral count or number of peptides, or Ion mobility behavior ofthe mass spectrometric or tandem mass spectrometric signal between aplurality of the distinct biomolecule corona signatures. In someembodiments, the computer is configured to process data from between 100and 2000 mass spectrometric or tandem mass spectrometric signals betweena plurality of the distinct biomolecule corona signatures. In someembodiments, the computer is configured to process the data comprisingthe intensities of between 10,000 and 5,000,000 mass spectrometric ortandem mass spectrometric signals between a plurality of the distinctbiomolecule corona signatures. In some embodiments, the biomoleculefingerprint is generated from data from a single mass spectrometric ortandem mass spectrometric run. In some embodiments, the single massspectrometric or tandem mass spectrometric run is performed in less thanone hour. In some embodiments, the computer is configured to identify abiomolecule or characterize an unidentified molecular feature based on amass spectrometric or tandem mass spectrometric signal and or ionmobility and chromatographic behavior, and wherein the computer providesa certainty threshold of at least 95% to identify a feature orcharacterize and unidentified feature. In some embodiments, theautomated system is configured to generate the biomolecule fingerprintfrom the complex biological sample in less than about 10 hours. In someembodiments, the determining comprises comparing the abundance of twobiomolecules whose concentrations span at least 7 to at least 12 ordersof magnitude in the complex biological sample.

In some embodiments, the computer is capable of distinguishing betweentwo or more biological states associated with biomolecule fingerprintsthat differ by less than 10%, 5%, 2%, or 1%. In some embodiments, thebiological state is a disease, disorder, or tissue abnormality. In someembodiments, the disease is an early phase or intermediate phase diseasestate. In some embodiments, the disease is cancer. In some embodiments,the cancer is a stage 0 cancer or a stage 1 cancer. In some embodiments,the cancer is selected from the group consisting of: lung cancer,pancreas cancer, myeloma, myeloid leukemia, meningioma, glioblastoma,breast cancer, esophageal squamous cell carcinoma, gastricadenocarcinoma, prostate cancer, bladder cancer, ovarian cancer, thyroidcancer, neuroendocrine cancer, colon carcinoma, ovarian cancer, head andneck cancer, Hodgkin's Disease, non-Hodgkin's lymphomas, rectum cancer,urinary cancers, uterine cancers, oral cancers, skin cancers, stomachcancer, brain tumors, liver cancer, laryngeal cancer, esophageal cancer,mammary tumors, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma,osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, Ewing'ssarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma,sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma,papillary adenocarcinomas, cystandeocarcinoma, medullary carcinoma,bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile ductcarcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor,cervical cancer, testicular tumor, endometrial cancer, lung carcinoma,small cell lung carcinoma, bladder carcinoma, epithelial carcinoma,glioblastomas, neuronomas, craniopharingiomas, schwannomas, glioma,astrocytoma, meningioma, melanoma, neuroblastoma, retinoblastoma,leukemias and lymphomas, acute lymphocytic leukemia and acute myelocyticpolycythemia vera, multiple myeloma, Waldenstrom's macroglobulinemia,and heavy chain disease, acute nonlymphocytic leukemias, chroniclymphocytic leukemia, chronic myelogenous leukemia, childhood-null acutelymphoid leukemia (ALL), thymic ALL, B-cell ALL, acute megakaryocyticleukemia, Burkitt's lymphoma, and T cell leukemia, small and largenon-small cell lung carcinoma, acute granulocytic leukemia, germ celltumors, endometrial cancer, gastric cancer, hairy cell leukemia, thyroidcancer and other cancers known in the art. In some embodiments, thebiological state is a pre-disease state.

Various aspects of the present disclosure provide a method fordistinguishing a biological state of a complex biological sample, themethod comprising: providing the complex biological sample to anautomated apparatus to generate a subset of biomolecules; assaying thesubset of biomolecules to generate a biomolecule fingerprint; anddistinguishing a biological state of the complex biological sample withthe biomolecule fingerprint.

In some embodiments, the biomolecule fingerprint comprises proteins. Insome embodiments, the subset of biomolecules from the complex biologicalsample comprises a lower ratio of albumin to non-albumin peptides thanthe complex biological sample. In some embodiments, the subset ofbiomolecules comprises biomolecules that span at least 6 to at least 12orders of magnitude in concentration range in the complex biologicalsample. In some embodiments, the subset of biomolecules comprisesproteins that span at least 6 to at least 12 orders of magnitude inconcentration range in the complex biological sample. In someembodiments, the biomolecule fingerprint comprises from 1 to 74,000protein groups.

In some embodiments, the assaying comprises desorbing a plurality ofbiomolecules from a biomolecule corona from among the plurality ofbiomolecule coronas. In some embodiments, the assaying compriseschemically modifying a biomolecule from among the plurality of desorbedbiomolecules. In some embodiments, the assaying comprises fragmenting abiomolecule from among the plurality of desorbed biomolecules. In someembodiments, the fragmenting comprises protease digestion. In someembodiments, the fragmenting comprises chemical peptide cleavage.

In some embodiments, the assaying comprises collecting the plurality ofdesorbed biomolecules. In some embodiments, the assaying comprisespurifying the collected plurality of desorbed biomolecules. In someembodiments, the purifying comprises solid-phase extraction. In someembodiments, the purifying depletes non-protein biomolecules from thecollected plurality of desorbed biomolecules. In some embodiments, theassaying comprises discarding the plurality of desorbed biomolecules. Insome embodiments, the assaying comprises desorbing a first subset ofbiomolecules and a second set of biomolecules from a biomolecule coronafrom among the plurality of biomolecule coronas, analyzing a biomoleculefrom among the first subset of biomolecules, and analyzing a biomoleculefrom among the second subset of biomolecules.

In some embodiments, the assaying comprises analyzing a biomoleculecorona from among the plurality of biomolecule coronas with massspectrometry, tandem mass spectrometry, mass cytometry, mass cytometry,potentiometry, fluorimetry, absorbance spectroscopy, Raman spectroscopy,chromatography, electrophoresis, immunohistochemistry, PCR, nextgeneration sequencing (NGS), or any combination thereof. In someembodiments, the assaying comprises mass spectrometry or tandem massspectrometry. In some embodiments, the assaying comprises identifyingthe conformational state of a protein from among the subset ofbiomolecules. In some embodiments, the assaying comprises identifying apost-translational modification on a protein from among the subset ofbiomolecules. In some embodiments, the distinguishing comprisescomparing the relative abundances of at least 200 to at least 1000biomolecules from the subset of biomolecules. In some embodiments, theassaying identifies biomolecules at concentrations of less than 10 ng/mLin the complex biological sample.

Various aspects of the present disclosure provide an automated apparatusfor generating a subset of biomolecules from a complex biologicalsample, the automated apparatus comprising: a plurality of particles andthe complex biological sample, wherein the automated apparatus isconfigured to generate the subset of biomolecules by contacting theplurality of particles with the complex biological sample to form aplurality of biomolecule coronas comprising the subset of biomolecules,and wherein a dynamic range of the subset of biomolecules is compressedrelative to a dynamic range of biomolecules present in the complexbiological sample. In some embodiments, the automated apparatuscomprises a substrate. In some embodiments, the substrate comprises amulti-well plate. In some embodiments, the substrate is a multi-wellplate. In some embodiments, the automated apparatus generates the subsetof biomolecules from a complex biological sample in less than 7 hours.

In some embodiments, the automated apparatus comprises an incubationelement. In some embodiments, the incubation element is configured toheat and/or incubate the plurality of particles and the complexbiological sample to a temperature between 4° C. and 40° C.

In some embodiments, the automated apparatus comprises at least onesolution selected from the group consisting of a wash solution, aresuspension solution, a denaturing solution, a buffer and a reagent. Insome embodiments, the resuspension solution comprises a Tris EDTAbuffer, a phosphate buffer, and/or water. In some embodiments, thedenaturing solution comprises a protease. In some embodiments, thedenaturing solution comprises a small molecule capable of performingpeptide cleavage.

In some embodiments, the automated apparatus comprises a loading unitcomprising a plurality of pipettes. In some embodiments, each pipette ofthe plurality of pipettes is configured to dispense about 5 uL-150 uL ofthe solution, the complex biological sample, and/or the plurality ofparticles. In some embodiments, the complex biological sample comprisesplasma, serum, urine, cerebrospinal fluid, synovial fluid, tears,saliva, whole blood, milk, nipple aspirate, ductal lavage, vaginalfluid, nasal fluid, ear fluid, gastric fluid, pancreatic fluid,trabecular fluid, lung lavage, sweat, crevicular fluid, semen, prostaticfluid, sputum, fecal matter, bronchial lavage, fluid from swabbings,bronchial aspirants, fluidized solids, fine needle aspiration samples,tissue homogenates, lymphatic fluid, cell culture samples, or anycombination thereof. In some embodiments, the automated apparatuscomprises a magnet. In some embodiments, the automated apparatuscomprises a filter.

In some embodiments, the compressed dynamic range comprises an increasein the number of types of biomolecules whose concentrations are within 4to 6 orders of magnitude of the most abundant biomolecule in the sample.In some embodiments, the types of biomolecules comprises protein. Insome embodiments, the dynamic range of the biomolecules of thebiomolecule coronas is a first ratio of a top decile of biomolecules toa bottom decile of biomolecules in the plurality of biomolecule coronas.In some embodiments, the generating enriches low abundance biomoleculesfrom the complex biological sample. In some embodiments, the lowabundance biomolecules are biomolecules at concentrations of 10 ng/mL orless in the complex biological sample.

In some embodiments, at least two particles from among the plurality ofparticles differ in at least one physicochemical property. In someembodiments, the at least one physicochemical property is selected fromthe group consisting of: composition, size, surface charge,hydrophobicity, hydrophilicity, surface functionality, surfacetopography, surface curvature, porosity, core material, shell material,shape, and any combination thereof. In some embodiments, a particle fromamong the plurality of particles is selected from the group consistingof: micelles, liposomes, iron oxide particles, silver particles, goldparticles, palladium particles, quantum dots, platinum particles,titanium particles, silica particles, metal or inorganic oxideparticles, synthetic polymer particles, copolymer particles, terpolymerparticles, polymeric particles with metal cores, polymeric particleswith metal oxide cores, polystyrene sulfonate particles, polyethyleneoxide particles, polyoxyethylene glycol particles, polyethylene imineparticles, polylactic acid particles, polycaprolactone particles,polyglycolic acid particles, poly(lactide-co-glycolide polymerparticles, cellulose ether polymer particles, polyvinylpyrrolidoneparticles, polyvinyl acetate particles, polyvinylpyrrolidone-vinylacetate copolymer particles, polyvinyl alcohol particles, acrylateparticles, polyacrylic acid particles, crotonic acid copolymerparticles, polyethlene phosphonate particles, polyalkylene particles,carboxy vinyl polymer particles, sodium alginate particles, carrageenanparticles, xanthan gum particles, gum acacia particles, Arabic gumparticles, guar gum particles, pullulan particles, agar particles,chitin particles, chitosan particles, pectin particles, karaya tumparticles, locust bean gum particles, maltodextrin particles, amyloseparticles, corn starch particles, potato starch particles, rice starchparticles, tapioca starch particles, pea starch particles, sweet potatostarch particles, barley starch particles, wheat starch particles,hydroxypropylated high amylose starch particles, dextrin particles,levan particles, elsinan particles, gluten particles, collagenparticles, whey protein isolate particles, casein particles, milkprotein particles, soy protein particles, keratin particles,polyethylene particles, polycarbonate particles, polyanhydrideparticles, polyhydroxyacid particles, polypropylfumerate particles,polycaprolactone particles, polyamine particles, polyacetal particles,polyether particles, polyester particles, poly(orthoester) particles,polycyanoacrylate particles, polyurethane particles, polyphosphazeneparticles, polyacrylate particles, polymethacrylate particles,polycyanoacrylate particles, polyurea particles, polyamine particles,polystyrene particles, poly(lysine) particles, chitosan particles,dextran particles, poly(acrylamide) particles, derivatizedpoly(acrylamide) particles, gelatin particles, starch particles,chitosan particles, dextran particles, gelatin particles, starchparticles, poly-β-amino-ester particles, poly(amido amine) particles,poly lactic-co-glycolic acid particles, polyanhydride particles,bioreducible polymer particles, and 2-(3-aminopropylamino)ethanolparticles, and any combination thereof.

In some embodiments, biomolecules of the biomolecule coronas comprise anumber of protein groups. In some embodiments, the number of proteingroups comprises from 1 to 20,000 protein groups. In some embodiments,the number of protein groups comprises from 100 to 10,000 proteingroups. In some embodiments, the number of protein groups comprises from100 to 5000 protein groups. In some embodiments, the number of proteinsgroups comprises from 300 to 2,200 protein groups. In some embodiments,the number of proteins groups comprises from 1,200 to 2,200 proteingroups.

In some embodiments, the automated apparatus comprises a purificationunit. In some embodiments, the purification unit comprises a solid phaseextraction (SPE) plate.

Various aspects of the present disclosure provide a method forgenerating a subset of biomolecules from a complex biological sample,the method comprising: providing the complex biological sample to anautomated apparatus, wherein the automated apparatus contacts thecomplex biological sample with a plurality of particles to generatebiomolecule coronas, wherein the automated apparatus processes thebiomolecule coronas to generate the subset of biomolecules, and whereina dynamic range of the subset of biomolecules is compressed relative toa dynamic range of biomolecules present in the complex biologicalsample.

In some embodiments, the method comprises assaying the subset ofbiomolecules to generate a biomolecule fingerprint. In some embodiments,the assaying identifies biomolecules at concentrations of less than 10ng/mL in the complex biological sample. In some embodiments, theassaying comprises analyzing biomolecule coronas with mass spectrometry,tandem mass spectrometry, mass cytometry, mass cytometry, potentiometry,fluorimetry, absorbance spectroscopy, Raman spectroscopy,chromatography, electrophoresis, immunohistochemistry, or anycombination thereof. In some embodiments, the assaying comprises massspectrometry or tandem mass spectrometry.

In some embodiments, the method comprises distinguishing a biologicalstate of the complex biological sample with the biomolecule fingerprint.In some embodiments, the biomolecule fingerprint comprises a pluralityof distinct biomolecule corona signatures. In some embodiments, thebiomolecule fingerprint comprises at least 5, 10, 20, 40, or 80, 150 or200 distinct biomolecule corona signatures. In some embodiments, thebiological state is a disease, disorder, or tissue abnormality. In someembodiments, the disease is an early phase or intermediate phase diseasestate. In some embodiments, the disease is cancer. In some embodiments,the cancer is a stage 0 cancer or a stage 1 cancer. In some embodiments,the cancer is selected from the group consisting of: lung cancer,pancreas cancer, myeloma, myeloid leukemia, meningioma, glioblastoma,breast cancer, esophageal squamous cell carcinoma, gastricadenocarcinoma, prostate cancer, bladder cancer, ovarian cancer, thyroidcancer, neuroendocrine cancer, colon carcinoma, ovarian cancer, head andneck cancer, Hodgkin's Disease, non-Hodgkin's lymphomas, rectum cancer,urinary cancers, uterine cancers, oral cancers, skin cancers, stomachcancer, brain tumors, liver cancer, laryngeal cancer, esophageal cancer,mammary tumors, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma,osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, Ewing'ssarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma,sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma,papillary adenocarcinomas, cystandeocarcinoma, medullary carcinoma,bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile ductcarcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor,cervical cancer, testicular tumor, endometrial cancer, lung carcinoma,small cell lung carcinoma, bladder carcinoma, epithelial carcinoma,glioblastomas, neuronomas, craniopharingiomas, schwannomas, glioma,astrocytoma, meningioma, melanoma, neuroblastoma, retinoblastoma,leukemias and lymphomas, acute lymphocytic leukemia and acute myelocyticpolycythemia vera, multiple myeloma, Waldenstrom's macroglobulinemia,and heavy chain disease, acute nonlymphocytic leukemias, chroniclymphocytic leukemia, chronic myelogenous leukemia, childhood-null acutelymphoid leukemia (ALL), thymic ALL, B-cell ALL, acute megakaryocyticleukemia, Burkitt's lymphoma, and T cell leukemia, small and largenon-small cell lung carcinoma, acute granulocytic leukemia, germ celltumors, endometrial cancer, gastric cancer, hairy cell leukemia, orthyroid cancer. In some embodiments, the biological state is apre-disease state.

Various aspects of the present disclosure provide an automated apparatusto identify proteins in a biological sample, the automated apparatuscomprising: a sample preparation unit; a substrate comprising aplurality of channels; a plurality of pipettes; a plurality ofsolutions, a plurality of nanoparticles, and wherein the automatedapparatus is configured to form a protein corona and digest the proteincorona.

Various aspects of the present disclosure provide an automated apparatusto identify proteins in a biological sample, the automated apparatuscomprising: a sample preparation unit; a substrate comprising aplurality of channels; a plurality of pipettes; a plurality ofsolutions, a plurality of nanoparticles, wherein the automated apparatusis configured to form a protein corona and digest the protein corona,and wherein at least one of the solutions is TE 150 mM KCl 0.05% CHAPSbuffer.

In some aspects, the sample preparation unit is configured to add theplurality of nanoparticles to the substrate with the plurality ofpipettes. In some aspects, the sample preparation unit is configured toadd the biological sample to the substrate with the plurality ofpipettes. In some aspects, the sample preparation unit is configured toincubate the plurality of nanoparticles and the biological sample toform the protein corona. In some aspects, the sample preparation unit isconfigured to separate the protein corona from the supernatant to form aprotein corona pellet. In some aspects, the sample preparation unit isconfigured to reconstitute the protein corona pellet with TE 150 mM KCl0.05% CHAPS buffer.

In some aspects, the automated apparatus comprises a magnetic source. Insome aspects, the automated apparatus is configured for BCA, gel, ortrypsin digestion of the protein corona. In some aspects, the automatedapparatus is enclosed. In some aspects, the automated apparatus issterilized before use. In some aspects, the automated apparatus isconfigured to a mass spectrometry. In some aspects, the automatedapparatus is temperature controlled.

Various aspects of the present disclosure provide a method ofidentifying proteins in a biological sample, the method comprising:adding the biological sample to an automated apparatus; generatingproteomic data from the automated apparatus; and quantifying theproteomic data. In some embodiments, the method further comprisesincubating a plurality of nanoparticles with the biological sample inthe automated apparatus to form a protein corona. In some embodiments,the method further comprises separating the protein corona from thesupernatant in the automated apparatus. In some embodiments, the methodfurther comprises digesting the protein corona to form the digestedsample in the automated apparatus. In some embodiments, the methodfurther comprises washing the digested sample in the automatedapparatus. In some embodiments, quantifying the proteomic data comprisesproviding the proteomic data to a mass spectrometry. In someembodiments, the biological sample is a biofluid. In some embodiments,the biofluid is serum or plasma.

In some aspects, the present disclosure provides an automated systemcomprising a network of units with differentiated functions indistinguishing states of a complex biological sample using a pluralityof particles having surfaces with different physicochemical propertieswherein: a first unit comprises a multichannel fluid transfer instrumentfor transferring fluids between units within the system; a second unitcomprises a support for storing a plurality of biological samples; athird unit comprises a support for a sensor array plate possessingpartitions that comprise the plurality of particles having surfaces withdifferent physicochemical properties for binding a population ofanalytes within the complex biological sample; a fourth unit comprisessupports for storing a plurality of reagents; a fifth unit comprisessupports for storing a reagent to be disposed of; a sixth unit comprisessupports for storing consumables used by the multichannel fluid transferinstrument; and wherein the system is programed to perform a series ofsteps comprising: contacting the complex biological sample with aspecified partition of the sensor array; incubating the complexbiological sample with the plurality of particles contained within thepartition of the sensor array plate; removing all components from apartition except the plurality of particles and a population of analytesinteracting with a particle; and preparing a sample for massspectrometry.

In some embodiments, the first unit comprises a degree of mobility thatenables access to all other units within the system. In someembodiments, the first unit comprises a capacity to perform pipettingfunctions.

In some embodiments, the support of the second and/or third unitcomprises support for a single plate, a 6 well plate, a 12 well plate, a96 well plate, or a rack of microtubes. In some embodiments, the secondand/or unit comprises a thermal unit capable of modulating thetemperature of said support and a sample. In some embodiments, thesecond and/or third unit comprises a rotational unit capable ofphysically agitating and/or mixing a sample.

In some embodiments, the plurality of particles having surfaces withdifferent physicochemical properties for binding a population ofanalytes within the complex biological sample are immobilized to asurface within a partition of the sensory array. In some embodiments,the plurality of particles comprises a plurality of magneticnanoparticles with different physicochemical properties for binding apopulation of analytes within the complex biological sample. In someembodiments, the system comprises a step wherein the sensor array plateis transferred to an additional seventh unit that comprises a magnetizedsupport and a thermal unit capable of modulating the temperature of saidsupport and a sample and incubated for an additional amount of time.

In some embodiments, the fourth unit comprises a set of reagents for:generating the sensor array plate; washing an unbound sample; and/orpreparing a sample for mass spectrometry. In some embodiments,contacting the biological sample with a specified partition of thesensor array comprises pipetting a specified volume of the biologicalsample into the specific partition of the sensor array. In someembodiments, contacting the biological sample with a specified partitionof the sensor array comprises pipetting a volume corresponding to a 1:1,1:2:1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:15, or 1:20 ratio of aplurality of particles in a solution to the biological sample.

In some embodiments, contacting the biological sample with a specifiedpartition of the sensor array comprises pipetting a volume of at least10 microliters, at least 50 microliters, at least 100 microliters, atleast 250 microliters, at least 500 microliters, or at least 1000microliters the biological sample into the specific partition of thesensor array.

In some embodiments, incubating the biological sample with the pluralityof particles contained within the partition of the sensor array platecomprises an incubation time of at least about 10 seconds, at leastabout 15 seconds, at least about 20 seconds, at least about 25 seconds,at least about 30 seconds, at least about 40 seconds, at least about 50seconds, at least about 60 seconds, at least about 90 seconds, at leastabout 2 minutes, at least about 3 minutes, at least about 4 minutes, atleast about 5 minutes, at least about 6 minutes, at least about 7minutes, at least about 8 minutes, at least about 9 minutes, at leastabout 10 minutes, at least about 15 minutes, at least about 20 minutes,at least about 25 minutes, at least about 30 minutes, at least about 45minutes, at least about 50 minutes, at least about 60 minutes, at leastabout 90 minutes, at least about 2 hours, at least about 3 hours, atleast about 4 hours, at least about 5 hours, at least about 6 hours, atleast about 7 hours, at least about 8 hours, at least about 9 hours, atleast about 10 hours, at least about 12 hours, at least about 14 hours,at least about 15 hours, at least about 16 hours, at least about 17hours, at least about 18 hours, at least about 19 hours, at least about20 hours, or at least about 24 hours.

In some embodiments, incubating the biological sample with the pluralityof particles contained within the partition of the substrate comprisesan incubation temperature between about 4° C. to about 40° C. Incubatingthe biological sample with the plurality of particles contained withinthe partition of the substrate may comprise an incubation temperaturebetween about 4° C. to about 37° C. Incubating the biological samplewith the plurality of particles contained within the partition of thesubstrate may comprise an incubation temperature between about 4° C. toabout 100° C.

In some embodiments, removing all components from a partition except theplurality of particles and a population of analytes interacting with aparticle comprises a series of wash steps.

In some embodiments, the second unit can facilitate a transfer of thesample for mass spectrometry to a mass spectrometry unit.

In some aspects, the present disclosure provides an automated apparatusto identify proteins in a biological sample, the automated apparatuscomprising: a sample preparation unit; a substrate comprising aplurality of channels; a plurality of pipettes; a plurality ofsolutions, a plurality of nanoparticles, and wherein the automatedapparatus is configured to form a protein corona and digest the proteincorona.

In some aspects, the present disclosure provides an automated apparatusto identify proteins in a biological sample, the automated apparatuscomprising: a sample preparation unit; a substrate comprising aplurality of channels; a plurality of pipettes; a plurality ofsolutions, a plurality of nanoparticles, wherein the automated apparatusis configured to form a protein corona and digest the protein corona,and wherein at least one of the solutions is TE 150 mM KCl 0.05% CHAPSbuffer.

In some embodiments, the sample preparation unit is configured to addthe plurality of nanoparticles to the substrate with the plurality ofpipettes. In some embodiments, wherein the sample preparation unit isconfigured to add the biological sample to the substrate with theplurality of pipettes. In some embodiments, the sample preparation unitis configured to incubate the plurality of nanoparticles and thebiological sample to form the protein corona.

In some embodiments, the sample preparation unit is configured toseparate the protein corona from the supernatant to form a proteincorona pellet. In some embodiments, the sample preparation unit isconfigured to reconstitute the protein corona pellet with TE 150 mM KCl0.05% CHAPS buffer.

In some embodiments, the automated apparatus further comprises amagnetic source. In some embodiments, the automated apparatus isconfigured for BCA, gel, or trypsin digestion of the protein corona.

In some embodiments, the automated apparatus is enclosed. In someembodiments, the automated apparatus is sterilized before use. In someembodiments, the automated apparatus is configured to a massspectrometry. In some embodiments, the automated apparatus istemperature controlled.

In some aspects, the present disclosure provides a method of identifyproteins in a biological sample, the method comprising: adding thebiological sample to the automated apparatus disclosed herein;generating proteomic data from the automated apparatus; and quantifyingthe proteomic data.

In some embodiments, the method further comprises incubating a pluralityof nanoparticles with the biological sample in the automated apparatusto form a protein corona. In some embodiments, the method furthercomprises separating the protein corona from the supernatant in theautomated apparatus. In some embodiments, the method further comprisesdigesting the protein corona to form the digested sample in theautomated apparatus.

In some embodiments, the method further comprises washing the digestedsample in the automated apparatus. In some embodiments, quantifying theproteomic data comprises providing the proteomic data to a massspectrometry.

In some embodiments, the biological sample is a biofluid. In someembodiments, the biofluid is serum or plasma.

Another aspect of the present disclosure provides a non-transitorycomputer readable medium comprising machine executable code that, uponexecution by one or more computer processors, implements any of themethods above or elsewhere herein.

Another aspect of the present disclosure provides a system comprisingone or more computer processors and computer memory coupled thereto. Thecomputer memory comprises machine executable code that, upon executionby the one or more computer processors, implements any of the methodsabove or elsewhere herein.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings (also “Figure” and “FIG.” herein), of which:

FIG. 1 shows a schematic illustration of the steps for generating datausing nanoparticle or protein corona methods.

FIG. 2 shows an example illustration of the steps for generating datausing nanoparticle or protein corona methods and units of the automatedsystem in which they can take place.

FIG. 3 shows an example layout of the system and coupling to acontinuous MS for high throughput applications.

FIG. 4 shows an example illustration of sensor array analyte capturemethods.

FIG. 5 shows a step-wise illustration of automated sample processing formagnetic sensor array particles.

FIG. 6 shows a step-wise illustration of automated sample processing forimmobilized sensor array particles.

FIG. 7 shows surface chemistries for magnetic nanoparticle sensorarrays.

FIG. 8 shows an example of protein corona-based methods for detectingdisease biomarkers in a cancer patient (referring to US20180172694A1,incorporated by reference in its entirety herein).

FIG. 9 shows a process for proteomic analysis. The process is tailoredfor high-throughput and automation that can be run in hours and acrossmultiple samples in parallel. The process includes particle-matrixassociation, particle wash (×3), formation of the protein corona,in-plate digestion, and mass spectrometry. Using the process, it maytake only 4 to 6 hours per batch of 96 samples. One nanoparticle, ormore, at a time may be incubated with a sample.

FIG. 10 shows the protein counts (number of proteins identified fromcorona analysis) collected on pluralities of particles comprising from 1particle type to 12 particle types. Each particle from among a pluralityof particles may be comprise unique materials, surfacefunctionalization, and/or physical property (e.g., size or shape).Pooled plasma from a group of healthy subjects was used. Counts are thenumbers of unique proteins collected from a plurality of particles andobserved in about 2 hour mass spectrometry (MS) runs. 1318 proteins wereidentified from the sample contacted with a plurality of particlescomprising 12 particle types.

FIG. 11 shows the distribution of the presence-filtered, clusterquality-filtered, median-normalized MS feature intensities for the56-sample NSCLC comparative study. Each line represents the density ofthe log 2 feature intensity for either a diseased sample or a controlsample. Density is plotted from 0.00 to 0.15 on the y-axis, and log 2feature intensity is plotted from 15 to 35 on the x-axis. At the highestpeak located near a log 2 feature intensity of about 28, with densitiesranging from about 0.13 to about 0.17, the two highest traces correspondto control samples, while the lowest trace corresponds to a diseasedsample. The remaining control and diseased traces are distributedbetween the highest and lowest traces. At the two shoulder peaks,occurring at about 20 log 2 feature intensity and about 23 log 2 featureintensity, the highest two traces are control traces and the lowest twotraces are control traces at the 20 log 2 feature intensity peak, andthe highest traces is a diseased trace at the 23 log 2 featureintensity.

FIG. 12 shows changed features in a non-small cell lung cancer (NSCLC)pilot study using poly(N-(3-(dimethylamino)propyl) methacrylamide)(PDMAPMA)-coated SPION particles. Seven MS features were identified asstatistically, significantly different between 28 subjects with Stage IVNSCLC (with associated co-morbidities and treatment effects) and 28 age-and gender-matched, apparently healthy subjects. The table at bottom isa list of the seven proteins that were significantly different. Thisincludes 5 known proteins and 2 unknown proteins. If a peptide-spectrummatch was made for MS2 data associated with the feature, that peptidesequence (and charge) as well as the potential parent protein areindicated; if an MS2 match was not associated with the feature, both thepeptide and the protein are marked as “Unknown”.

FIG. 13 shows correlation of the maximum intensities of particle coronaproteins and plasma proteins to the published concentration of the sameproteins. The blue plotted lines are linear regression models to thedata and the shaded regions represent the standard error of the modelfit. The dynamic range of the samples assayed with particles (“S-003,”“S-007,” and “S-011”, detailed in TABLE 1) exhibited a compresseddynamic range as compared to the plasma sample not assayed withparticles (“Plasma”), as shown by the decrease in slopes of the linearfits. The slopes of each plot are 0.47, 0.19, 0.22, and 0.18 for, plasmawithout particles, plasma with S-003 particles, plasma with S-007particles, and plasma with S-011 particles, respectively.

FIG. 14 shows the dynamic range compression of a protein corona analysisassay with mass spectrometry as compared to mass spectrometry withoutparticle corona formation. Protein intensities of common proteinsidentified in particle corona in the plasma samples assayed in FIG. 13(“Nanoparticle MS ln Intensity”) are plotted against the proteinintensity identified by mass spectrometry of plasma without particles(“Plasma MS ln Intensity”). The lightest dotted line shows a slope of 1,indicating the dynamic range of mass spectrometry without particles. Theslopes of the linear fits to the protein intensity was 0.12, 0.36, and0.093 for S-003, S-007, and S-011 particles, respectively. The grayedarea indicates the standard error region of the regression fit.

DETAILED DESCRIPTION

While various embodiments of the invention have been shown and describedherein, it will be obvious to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions may occur to those skilled in the art withoutdeparting from the invention. It should be understood that variousalternatives to the embodiments of the invention described herein may beemployed.

Whenever the term “at least,” “greater than,” or “greater than or equalto” precedes the first numerical value in a series of two or morenumerical values, the term “at least,” “greater than” or “greater thanor equal to” applies to each of the numerical values in that series ofnumerical values. For example, greater than or equal to 1, 2, or 3 isequivalent to greater than or equal to 1, greater than or equal to 2, orgreater than or equal to 3.

Whenever the term “no more than,” “less than,” or “less than or equalto” precedes the first numerical value in a series of two or morenumerical values, the term “no more than,” “less than,” or “less than orequal to” applies to each of the numerical values in that series ofnumerical values. For example, less than or equal to 3, 2, or 1 isequivalent to less than or equal to 3, less than or equal to 2, or lessthan or equal to 1.

As used herein, a “feature” identified by mass spectrometry includes asignal at a specific combination of retention time and m/z(mass-to-charge ratio), where each feature has an associated intensity.Some features are further fragmented in a second mass spectrometryanalysis (MS2) for identification.

As used herein, the term “sensor element” refers to elements that areable to bind to a plurality of biomolecules when in contact with asample and encompasses the term “nanoscale sensor element”. A sensorelement may be a particle, such as a nanoparticle, or microparticle. Asensor element may be a surface or a portion of a surface. A sensorelement may comprise a particle or plurality of particles. A sensorelement may comprise a plurality of surfaces capable of adsorbing orbinding biomolecules. A sensor element may comprise a porous material,such as a material into which biomolecules can intercalate.

As used herein, a “sensor array” may comprise a plurality of sensorelements wherein the plurality of sensor elements (e.g., particles)comprises multiple types of sensor elements. The sensor elements may bedifferent types that differ from each other in at least onephysicochemical property. A sensor array may be a substrate with aplurality of partitions containing a plurality of sensor elements (e.g.,particles). For example, a sensor array may comprise a multi-well platewith a plurality of particles distributed between the plurality ofwells. A sensor array may be a substrate comprising a plurality ofpartitions, wherein the plurality of partitions comprises a plurality ofparticles. In some embodiments, each sensor element or particle is ableto bind a plurality of biomolecules in a sample to produce a biomoleculecorona signature. In some embodiments, each sensor element (e.g.,particle type) has a distinct biomolecule corona signature.

As used herein, the term “biomolecule corona” refers to the plurality ofdifferent biomolecules that bind to a sensor element. The term“biomolecule corona” may refer to the proteins, lipids and other plasmacomponents that bind to particles (e.g., nanoparticles) when they comeinto contact with biological samples or biological system. For useherein, the term “biomolecule corona” also encompasses both the soft andhard protein corona as referred to in Milani et al. “Reversible versusIrreversible Binding of Transferring to Polystyrene Nanoparticles: Softand Hard Corona” ACS NANO, 2012, 6(3), pp. 2532-2541; Mirshafiee et al.“Impact of protein pre-coating on the protein corona composition andnanoparticle cellular uptake” Biomaterials vol. 75, January 2016 pp.295-304, Mahmoudi et al. “Emerging understanding of the protein coronaat the nano-bio interfaces” Nanotoday 11(6) December 2016, pp. 817-832,and Mahmoudi et al. “Protein-Nanoparticle Interactions: Opportunitiesand Challenges” Chem. Rev., 2011, 111(9), pp. 5610-5637, the contents ofwhich are incorporated by reference in their entireties. As describedtherein, an adsorption curve may show the build-up of a strongly boundmonolayer up to the point of monolayer saturation (at a geometricallydefined protein-to-NP ratio), beyond which a secondary, weakly boundlayer is formed. While the first layer is irreversibly bound (hardcorona), the secondary layer (soft corona) may exhibit dynamic exchange.Proteins that adsorb with high affinity may form the “hard” corona,comprising tightly bound proteins that do not readily desorb, andproteins that adsorb with low affinity may form the “soft” corona,comprising loosely bound proteins. Soft and hard corona can also becharacterized based on their exchange times. Hard corona may show muchlarger exchange times in the order of several hours. See, e.g., M.Rahman et al. Protein-Nanoparticle Interactions, Spring Series inBiophysics 15, 2013, incorporated by reference in its entirety.

The term “biomolecule” refers to biological components that may beinvolved in corona formation, including, but not limited to, forexample, proteins, polypeptides, polysaccharides, a sugar, a lipid, alipoprotein, a metabolite, an oligonucleotide, metabolome or combinationthereof. It is contemplated that the biomolecule coronas of distinctparticles may contain some of the same biomolecules, may containdistinct biomolecules with regard to the other sensor elements, and/ormay differ in level or quantity, type or conformation of the biomoleculethat binds to each sensor element. In one embodiment, the biomolecule isselected from the group of proteins, nucleic acids, lipids, andmetabolomes.

The term “biomolecule corona signature” refers to the composition,signature or pattern of different biomolecules that are bound to eachtype of particle or separate sensor element. The signature may not onlyrefers to the different biomolecules but also the differences in theamount, level or quantity of the biomolecule bound to the sensorelement, or differences in the conformational state of the biomoleculethat is bound to the particle or sensor element. It is contemplated thatthe biomolecule corona signatures of each distinct type of sensorelements may contain some of the same biomolecules, may contain distinctbiomolecules with regard to the other sensor elements, and/or may differin level or quantity, type or conformation of various biomolecules. Thebiomolecule corona signature may depend on not only the physicochemicalproperties of the sensor element (e.g., particle), but also the natureof the sample and the duration of exposure to the biological sample.

Disclosed herein are compositions and methods for multi-omic analysis.“Multi-omic(s)” or“multiomic(s)” can refer to an analytical approach foranalyzing biomolecules at a large scale, wherein the data sets aremultiple omes, such as proteome, genome, transcriptome, lipidome, andmetabolome. Non-limiting examples of multi-omic data include proteomicdata, genomic data, lipidomic data, glycomic data, transcriptomic data,or metabolomics data.

“Biomolecule” in “biomolecule corona” can refer to any molecule orbiological component that can be produced by, or is present in, abiological organism. Non-limiting examples of biomolecules includeproteins (protein corona), polypeptides, oligopeptides, polyketides,polysaccharides, a sugar, a lipid, a lipoprotein, a metabolite, anoligonucleotide, a nucleic acid (DNA, RNA, micro RNA, plasmid, singlestranded nucleic acid, double stranded nucleic acid), metabolome, aswell as small molecules such as primary metabolites, secondarymetabolites, and other natural products, or any combination thereof. Insome embodiments, the biomolecule is selected from the group ofproteins, nucleic acids, lipids, and metabolomes.

Currently, there are a small number of protein-based biomarkers in usetoday for clinical diagnosis, and in spite of extensive efforts toanalyze the plasma proteome for the expansion of markers, relatively fewnew candidates have been accepted as clinically useful indicators. Theplasma proteome contains >10,000 proteins and potentially an order ofmagnitude more protein isoforms with a concentration range spanning over10 orders of magnitude (from mg/mL to pg/mL). These attributes, combinedwith a lack of convenient molecular tools for proteome analysis, makecomprehensive studies of the plasma proteome exceptionally challenging.Approaches to overcome the broad dynamic range of proteins in biologicalsamples must be capable of identifying and quantifying against abackground of thousands of unique proteins and even more proteinvariants. However, there are no existing technologies that are capableof simultaneous measurement of proteins across the entire plasmaconcentration range in a format with a sufficient throughput and with apractical cost profile to allow for appropriately-sized studies withrobust prospects for validation and replication. These challenges notonly limit the discovery of novel disease biomarkers, but have been abottleneck against the adoption of proteogenomics and protein annotationof genomic variants. Advances in mass spectrometry (MS) along withdevelopment of improved data analytics have offered tools for deep andbroad proteomic analysis. Several attempts have been made tosubstantially improve the detection of low abundance proteins, such asdepletion of highly abundant proteins, plasma fractionation, and peptidefractionation. It is now possible to identify over 4,500 proteins inplasma. However, current approaches are fairly complex andtime-consuming (days to weeks), and thus require a tradeoff betweendepth of protein coverage and sample throughput. Consequently, a simpleand robust strategy for comprehensive and rapid analysis of theavailable body of information in the proteome remains an unmet need.

Additionally, the earlier a disease is diagnosed, the more likely thatthe disease can be cured or successfully managed leading to a betterprognosis for the patient. When a disease is treated early, it may bepossible to prevent or delay problems from the disease and may improvethe outcomes for the patient, including extending the patient's lifeand/or quality of life.

Early diagnosis of cancer is crucial, as many types of cancers can besuccessfully treated in their early stages. For example, five-yearsurvival after early diagnosis and treatment of breast, ovarian, andlung cancers is 90%, 90%, and 70%, respectively, compared to 15%, 5%,and 10% for patients diagnosed at the most advanced stage of disease.Once cancer cells leave their tissue of origin, successful treatmentusing available established therapeutics becomes very unlikely. Althoughrecognizing the warning signs of cancers and taking prompt action maylead to early diagnosis, the majority of cancers (e.g., lung) showsymptoms only after cancer cells have already invaded the surroundingtissues and metastasized throughout the body. For example, more than 60%of patients with breast, lung, colon, and ovarian cancer have concealedor even metastatic colonies by the time their cancers are detected.Therefore, there is an urgent need for development of an effectiveapproach for early detection of cancer. Such an approach should have thesensitivity to identify a cancer at various stages and the specificityto give a negative result when the person being tested is free of thecancer. There have been extensive efforts to develop methods for earlydetection of cancers; although huge numbers of risk factors andbiomarkers have been introduced, a broadly relevant platform for earlydetection of a wide range of cancers remains elusive.

As various types of cancers can change the composition of bloodplasma—even in their early stages—one promising approach for earlydetection is molecular blood analysis for biomarkers. Although thisstrategy has already worked for a few cancers (like PSA for prostatecancer), there are not yet specific biomarkers for early detection ofthe majority of cancers. For such cancers (e.g., lung), none of thedefined candidate circulating biomarkers has been clinically validated,and very few have reached late-stage clinical development. Therefore,there is an urgent need for novel approaches to improve our ability todetect cancer, as well as other diseases, at very early stages.

Automated Sample Preparation

The present disclosure provides systems and methods for automated samplepreparation, data generation, protein corona analysis. As is depicted inFIG. 1 , the systems and methods can comprise (1) contacting a sample toparticles (e.g., in a particle mixture) on a sensor array, substrate,plate, or within partitions on any of the foregoing, (2) allowingbiomolecules in the sample to bind to the particles, (3) removingunbound sample from the particles, and (4) preparing a sample foranalysis (e.g., using mass spectrometry (“MS”)). For example, in (1), amethod of the present disclosure can comprise contacting a biologicalsample to a plurality of particles. In (2), the sample may be incubatedwith the plurality of particles so as to promote biomolecule adsorptionto the particles. In (3), unbound sample may be removed while retainingthe particles and the biomolecules adsorbed to the particles. In (4) theadsorbed biomolecules may be desorbed from the particles and preparingthem for mass spectrometric analysis by which example data may begenerated.

The present disclosure provides automated systems, methods and kits forbiomolecule corona preparation and analysis. The automated apparatus mayperform at least the aforementioned data generating steps outlined inFIG. 1 using various units illustrated in FIG. 2 . The automatedapparatus may contain a substrate with a plurality of partitionscontaining sensor elements 205 and a biological sample 210. The loadingunit 215 on the apparatus may transfer a portion of the biologicalsample 210 into a partition on the substrate 205, leading to adsorptionof biomolecules from the biological sample onto a sensor element in thepartition on the substrate. The automated apparatus may then removeunbound biomolecules from the partition, optionally transferring theunbound sample into a waste receptacle 220. The remaining biomolecules(e.g., biomolecules adsorbed to the sensor element) may be desorbed,collected, and prepared for mass spectrometric analysis. The reagents225 may comprise a buffer, such as a resuspension buffer capable ofdesorbing biomolecules from a biomolecule corona or a denaturationbuffer capable of denaturing or fragmenting a biomolecule. Reagents(e.g., a buffer or protease) 225 may also be loaded using the loadingunit 215 to facilitate any of the foregoing.

In some aspects, the present disclosure provides an automated systemcomprising a network of units with differentiated functions indistinguishing states of a complex biological sample using a pluralityof particles having surfaces with different physicochemical propertieswherein: a first unit comprises a multichannel fluid transfer instrumentfor transferring fluids between units within the system; a second unitcomprises a support for storing a plurality of biological samples; athird unit comprises a support for a sensor array plate (e.g., asubstrate comprising a plurality of partitions comprising sensorelements, such as a 96 well plate containing nanoparticles) possessingpartitions that comprise the plurality of particles having surfaces withdifferent physicochemical properties for detecting a binding interactionbetween a population of analytes within the complex biological sampleand the plurality of particles; a fourth unit comprises supports forstoring a plurality of reagents; a fifth unit comprises supports forstoring a reagent to be disposed of; a sixth unit comprises supports forstoring consumables used by the multichannel fluid transfer instrument;and wherein the system is programed to perform a series of stepscomprising: contacting the biological sample with a specified partitionof the sensor array; incubating the biological sample with the pluralityof particles contained within the partition of the sensor array plate;removing all components from a partition except the plurality ofparticles and a population of analytes interacting with a particle; andpreparing a sample for mass spectrometry.

An example of such an apparatus is provided in FIG. 3 . The apparatuscomprises an automated pipette that is able to transfer volumes betweena biological sample storage unit, a substrate comprising a plurality ofpartitions comprising a plurality of sensor elements, a waste collectionunit, a unit comprising a denaturation solution, and a unit comprising aresuspension solution. The automated apparatus can perform a biomoleculecorona assay which comprises transferring a portion of the biologicalsample into a partition within the substrate comprising a sensorelement, incubating the portion of the sample with the sensor element toallow biomolecules from the biological sample to bind to the sensorelement, removing contents from the partition comprising biomoleculesthat are not bound to the sensor elements, and then preparing thebiomolecules that remained within the partition for mass spectrometric(MS) analysis (e.g., LC-MS).

The loading may comprise a degree of mobility that enables access to allother unit within the system. The loading may comprise a capacity toperform pipetting functions.

The system or apparatus of the present disclosure may comprise supportfor a single plate, a 6 well plate, a 12 well plate, a 96 well plate, a192 well plate, a 384 well plate, or a rack of microtubes. In someembodiments, the system or apparatus of the present disclosure maycomprise a thermal unit capable of modulating the temperature of saidsupport and a sample. In some embodiments, the system or apparatus ofthe present disclosure may comprise a rotational unit capable ofphysically agitating and/or mixing a sample.

In some embodiments, the plurality of particles comprises surfaces withdifferent physicochemical properties for detecting a binding interactionbetween a population of analytes within the complex biological sampleand the plurality of particles are immobilized to a surface with apartition of the sensory array. In some embodiments, the plurality ofparticles comprises a plurality magnetic nanoparticles in a solutionwith different physicochemical properties for detecting a bindinginteraction between a population of analytes within the complexbiological sample and the plurality of particles. In some embodiments,the system comprises a step wherein the sensor array plate istransferred to an additional seventh unit that comprises a magnetizedsupport and a thermal unit capable of modulating the temperature of saidsupport and a sample and incubated for an additional amount of time.

In some embodiments, the fourth unit comprises a set of reagents for:generating the sensor array plate; washing an unbound sample; and/orpreparing a sample for mass spectrometry. In some embodiments,contacting the biological sample with a specified partition of thesensor array comprises pipetting a specified volume of the biologicalsample into the specific partition of the sensor array. In someembodiments, contacting the biological sample with a specified partitionof the sensor array comprises pipetting a volume corresponding to a 1:1,1:2:1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:15, or 1:20 ratio of aplurality of particles in a solution to the biological sample.

In some embodiments, contacting the biological sample with a specifiedpartition of the sensor array comprises pipetting a volume of at least10 microliters, at least 50 microliters, at least 100 microliters, atleast 250 microliters, at least 500 microliters, or at least 1000microliters the biological sample into the specific partition of thesensor array.

Automated Apparatus

In some aspects, the present disclosure provides an automated apparatusfor generating a subset of biomolecules from a biological sample,comprising: a substrate comprising a plurality of partitions, a firstunit comprising the biological sample, and a loading unit that ismovable across the substrate and is capable of transferring a volume(e.g., a volume of buffer) between different units of the apparatus. Insome cases, the substrate is a multi-well plate.

The plurality of partitions may comprise a plurality of sensor elements.The plurality of sensor elements may comprise particles. The pluralityof sensor elements may be particles (e.g., nanoparticles ormicroparticles).

A partition from among the plurality of partitions may comprise 1 to 100types of sensor elements (e.g., distinct particle types). A partitionfrom among the plurality of partitions may comprise 2 to 50 types ofsensor elements. A partition from among the plurality of partitions maycomprise 2 to 5 types of sensor elements. A partition from among theplurality of partitions may comprise 3 to 8 types of sensor elements. Apartition from among the plurality of partitions may comprise 4 to 10types of sensor elements. A partition from among the plurality ofpartitions may comprise 5 to 12 types of sensor elements. A partitionfrom among the plurality of partitions may comprise 6 to 15 types ofsensor elements. A partition from among the plurality of partitions maycomprise 8 to 20 types of sensor elements.

Two or more partitions from among the plurality of partitions maycomprise different quantities of sensor elements. two or more partitionsfrom among the plurality of partitions may comprise different types ofsensor elements. A partition amongst a plurality of partitions maycomprise a combination of types and/or quantities of sensor element(s)that differs from other partitions in the plurality. A subset ofpartitions in a plurality of partitions may each contain a combinationof distinct sensor elements that is distinct from other partitions inthe plurality.

Sensor elements may be stored in dry form inside of or within thepartitions. Dry sensor elements may be reconstituted or rehydrated priorto use. Sensor elements may also be stored within solutions. Forexample, a substrate partition may comprise a solution comprising a highconcentration of particles.

Partitions from among the plurality of partitions comprise differentconcentrations or amounts (e.g., by mass/molar amount per unit volume ofsample) of sensor elements. A partition from among the plurality ofpartitions may comprise from 1 pM to 100 nM of sensor elements. Apartition from among the plurality of partitions comprise may from 1 pMto 500 pM of sensor elements. A partition from among the plurality ofpartitions may comprise from 10 pM to 1 nM of sensor elements. Apartition from among the plurality of partitions may comprise from 100pM to 10 nM of sensor elements. A partition from among the plurality ofpartitions may comprise from 500 pM to 100 nM of sensor elements. Apartition from among the plurality of partitions may comprise from 50μg/ml to 300 μg/ml of sensor elements. A partition from among theplurality of partitions may comprise from 100 μg/ml to 500 μg/ml ofsensor elements. A partition from among the plurality of partitions maycomprise from 250 μg/ml to 750 μg/ml of sensor elements. A partitionfrom among the plurality of partitions may comprise from 400 μg/ml to 1mg/ml of sensor elements. A partition from among the plurality ofpartitions may comprise from 600 μg/ml to 1.5 mg/ml of sensor elements.A partition from among the plurality of partitions may comprise from 800μg/ml to 2 mg/ml of sensor elements. A partition from among theplurality of partitions may comprise from 1 mg/ml to 3 mg/ml of sensorelements. A partition from among the plurality of partitions maycomprise from 2 mg/ml to 5 mg/ml of sensor elements. A partition fromamong the plurality of partitions may comprise more than 5 mg/ml ofsensor elements.

The loading unit may be configured to move between and transfer volumes(e.g., a volume of a solution or a powder) between any units,compartments, or partitions within the apparatus. The loading unit maybe configured to move precise volumes (e.g., within 0.1%, 0.01%, 0.001%of the specified volume). The loading unit may be configured to collecta volume from the substrate or a compartment or partition within thesubstrate, and dispense the volume back into the substrate orcompartment or partition within the substrate, or to dispense the volumeor a portion of the volume into a different unit, compartment, orpartition. The loading unit may be configured to move multiple volumessimultaneously, such as 2 to 400 separate volumes. The loading unit maycomprise a plurality of pipette tips.

The loading unit may be configured to move a volume of a liquid. Thevolume may be about 0.1 μl, 0.2 μl, 0.3 μl, 0.4 μl, 0.5 μl, 0.6 μl, 0.7μl, 0.8 μl, 0.9 μl, 1 μl, 2 μl, 3 μl, 4 μl, 5 μl, 6 μl, 7 μl, 8 μl, 9μl, 10 μl, 12 μl, 15 μl, 20 μl, 25 μl, 30 μl, 40 μl, 50 μl, 60 μl, 70μl, 80 μl, 90 μl, 100 μl, 120 μl, 150 μl, 180 μl, 200 μl, 250 μl, 300μl, 400 μl, 500 μl, 600 μl, 800 μl, 1 ml, or more than 1 ml. The liquidmay be a biological sample or a solution.

In some cases, the solution comprises a wash solution, a resuspensionsolution, a denaturing solution, a buffer, a reagent (e.g., a reducingreagent), or any combination thereof. In some cases, the solutioncomprises a biological sample.

In part owing to these functionalities, the loading unit can be capableof partitioning a sample. In some embodiments, this comprises dividing asample into a number of partitions. A sample can be divided into atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 40, 50, 60, 70,80, 90, 100, 120, 150, 180, 200, 250, 300, 350, 400, 500, or morepartitions. A sample can be divided into 96, 192, or 384 partitions. Theautomated apparatus can comprise multiple substrates comprisingpartitions. The automated apparatus may comprise 1, 2, 3, 4, 5, or moresubstrates comprising partitions. In some cases, the loading unit loadsdifferent volumes of the biological sample into different partitions. Insome cases, the loading unit loads identical volumes into two or morepartitions. The volume of biological sample loaded into a partition maybe about 0.1 μl, 0.2 μl, 0.3 μl, 0.4 μl, 0.5 μl, 0.6 μl, 0.7 μl, 0.8 μl,0.9 μl, 1 μl, 2 μl, 3 μl, 4 μl, 5 μl, 6 μl, 7 μl, 8 μl, 9 μl, 10 μl, 12μl, 15 μl, 20 μl, 25 μl, 30 μl, 40 μl, 50 μl, 60 μl, 70 μl, 80 μl, 90μl, 100 μl, 120 μl, 150 μl, 180 μl, 200 μl, 250 μl, 300 μl, 400 μl, 500μl, 600 μl, 800 μl, 1 ml, or more than 1 ml. The volume of biologicalsample loaded into a partition may be about 10 μl to 400 μl. The volumeof biological sample loaded into a partition may be about 5 μl to 150μl. The volume of biological sample loaded into a partition may be about35 μl to 80 μl. In some cases, the loading unit may partition two ormore biological samples. For example, a sample storage unit may comprisetwo biological samples that the system partitions into one well plate.In some embodiments, the loading unit can facilitate a transfer of thesample for mass spectrometry to a mass spectrometry unit.

The system may be configured to perform a dilution on a sample or asample partition. A sample or sample partition may be diluted withbuffer, water (e.g., purified water), a non-aqueous solvent, or anycombination thereof. The diluent may be stored in the automatedapparatus prior to dispensation into a substrate partition. Theautomated apparatus may store a plurality of diluents differing in pH,salinity, osmolarity, viscosity, dielectric constant, or any combinationthereof. The diluents may be used to adjust the chemical properties of asample or sample partition. The automated apparatus may dilute a sampleor sample partition by 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 8-fold,10-fold, 15-fold, 20-fold, 30-fold, 40-fold, 50-fold, 75-fold, 100-fold,150-fold, 200-fold, 300-fold, 400-fold, 500-fold or greater. Theautomated apparatus may perform different dilutions on two samples orsample partitions. The system may perform different dilutions on eachpartition from among a plurality of partitions. For example, the systemmay perform different dilutions on each of the 96 sample partitions in a96 well plate. In some cases, the different dilutions comprise differentdegrees of dilution (e.g., 2-fold vs. 4-fold). In some cases, thedifferent dilutions comprise dilution with different solutions (e.g.,different buffers). In some cases, two sample partitions may be made todiffer in one or more chemical properties, such as pH, salinity, orviscosity.

In some cases, the system may modify the chemical composition of asample or sample partition. The system may modify or adjust the pH,salinity, osmolarity, dielectric constant, viscosity, buffer types, salttypes, sugar types, detergent types, or any combination thereof for asample or sample partition. Such modification or adjustments maycomprise mixing a reagent from the fourth unit with a sample or samplepartition. The system may differently modify the chemical composition oftwo samples or sample partitions.

A system or automated apparatus of the present disclosure may alsocomprise an incubation element. The incubation element may contact,support, or hold another component of the automated apparatus (e.g., thesubstrate or a unit). The incubation unit may contact, support, or holdmultiple components of the automated apparatus. The incubation elementmay contact the substrate to facilitate heat transfer between theincubation element and the substrate. The incubation unit may beconfigured to control the temperature of the one or more components ofthe automated apparatus, such as by heating or cooling. The incubationelement may be capable of cooling a component of the apparatus to from20° C. to 1° C. The incubation element may be capable of heating acomponent of the apparatus to from 25° C. to 100° C. The incubationelement may be capable of setting the temperature a component of theapparatus to from 4° C. to 37° C. The incubation element may beconfigured to heat or cool different portions of a component of theautomated apparatus to different temperatures. For example, theincubation element may hold a first partition in the substrate at 30° C.and a second partition in the substrate at 35° C. The incubation elementmay control the temperature of a sample or partition. The incubationelement may comprise a temperature sensor (e.g., a thermocouple) fordetecting the temperature within a partition or container. Theincubation element may calibrate its heating or cooling to the readoutfrom the temperature sensor.

The incubation element may be configured to physically agitate acomponent of the automated apparatus. The agitation may be in the formof shaking or spinning, vibrating, rocking, sonicating, or anycombination thereof. The incubation element may be capable of providingmultiple agitation intensities and/or frequencies. For example, theincubation element may comprise multiple settings for shaking atdifferent frequencies and amplitudes. The incubation element may also becapable of stirring and or mixing a volume (e.g., a portion of thebiological sample).

The automated apparatus may comprise a unit comprising a resuspensionsolution. The loading unit may be capable of transferring a volume ofthe resuspension solution to a partition from among the plurality ofpartitions of the substrate. In some cases, this results in the dilutionof a sample present within the partition and can further result in thedesorption of a plurality of biomolecules from a biomolecule coronadisposed on a sensor element within the partition. The quantity ofbiomolecules desorbed from a biomolecule corona can depend on the volumeof the resuspension solution added to the partition, the temperature ofthe partition, the composition of the resuspension solution (e.g., thesalinity, osmolarity, viscosity, dielectric constant, or pH), the volumeof the biological sample within the partition, and the sensor elementtype and the composition of biomolecules in the biomolecule corona. Thetransfer of a volume of the resuspension solution into a partition mayresult in the desorption of less than 5% of the biomolecules from abiomolecule corona. The transfer of a volume of the resuspensionsolution into a partition may result in the desorption of 10% to 20% ofthe biomolecules from a biomolecule corona. The transfer of a volume ofthe resuspension solution into a partition may result in the desorptionof 20% to 30% of the biomolecules from a biomolecule corona. Thetransfer of a volume of the resuspension solution into a partition mayresult in the desorption of 30% to 40% of the biomolecules from abiomolecule corona. The transfer of a volume of the resuspensionsolution into a partition may result in the desorption of 40% to 50% ofthe biomolecules from a biomolecule corona. The transfer of a volume ofthe resuspension solution into a partition may result in the desorptionof 50% to 60% of the biomolecules from a biomolecule corona. Thetransfer of a volume of the resuspension solution into a partition mayresult in the desorption of 60% to 70% of the biomolecules from abiomolecule corona. The transfer of a volume of the resuspensionsolution into a partition may result in the desorption of 70% to 80% ofthe biomolecules from a biomolecule corona. The transfer of a volume ofthe resuspension solution into a partition may result in the desorptionof 80% to 90% of the biomolecules from a biomolecule corona. Thetransfer of a volume of the resuspension solution into a partition mayresult in the desorption of more than 90% of the biomolecules from abiomolecule corona.

In some cases, multiple rounds of desorption are performed. In eachround, the supernatant comprising the desorbed biomolecules may becollected, analyzed, or discarded. The types and abundances ofbiomolecules in the supernatant may differ between desorption rounds.The automated apparatus may perform one or more desorption and discardcycles (i.e., washes), followed by one or more desorption cyclescomprising sample collection and/or analysis.

The resuspension solution may be tailored to optimize enrichment ofparticular biomarkers. The resuspension solution may comprise a buffer,such as Tris-EDTA (TE), CHAPS, PBS, citrate, HEPES, MES, CHES, oranother bio buffer. The resuspension solution may comprise Tris EDTA(TE) 150 mM KCl 0.05% CHAPS buffer. The resuspension solution maycomprise 10 mM TrisHCl pH 7.4, 1 mM EDTA. The resuspension solution mayalso contain or be highly purified water (e.g., distilled or deionizedwater). Biomolecule desorption may be augmented by heating or agitationby an incubation element. The supernatant may be transferred to a newpartition following desorption. A resuspension solution may be used todilute a sample.

The automated apparatus may comprise a unit comprising a denaturingsolution. The denaturing solution may comprise a protease. Thedenaturing solution may comprise a chemical capable of performingpeptide cleavage (e.g., cyanogen bromide, formic acid, or hydroxylamine,2-nitro-5-thiocyanatobenzoic acid). The denaturing solution may comprisea chemical denaturant such as guanidine, urea, sodium deoxycholate,acetonitrile, trichloroacetic acid, acetic acid, sulfosalicylic acid,sodium bicarbonate, ethanol, perchlorate, dodecyl sulfate, or anycombination thereof. The denaturing solution may comprise a reductant,such as 2-mercaptoethanol, dithiothreitol, ortris(2-carboxyethyl)phosphine. The protease may be trypsin. Thedenaturing solution may be added to a partition following desorption.The denaturing solution may be added to a partition comprisingbiomolecule coronas.

The automated apparatus may comprise a magnet or array of magnets. Theautomated apparatus may capable of moving the substrate onto and off ofthe magnet or array of magnets. The array of magnets may be structuredso that a plurality of magnets from the array of magnets can restdirectly underneath a plurality of partitions from the substrate. Themagnet may be capable of immobilizing magnetic sensor elements (e.g.,magnetic particles such as coated or uncoated super paramagnetic ironoxide nanoparticles) within a partition on the substrate. For example,the magnet may prevent magnetic nanoparticles from being removed from apartition during a wash step. The magnet may also create a pellet from acollection of magnetic particles. The magnet may create a particlepellet in less than 10 minutes. The magnet may create a particle pelletin less than 5 minutes. The particle pellet may comprise a particle witha biomolecule corona.

The automated apparatus may comprise a purification unit. Thepurification unit may comprise a plurality of partitions comprising anadsorbent or resin. The purification unit may comprise a solid-phaseextraction array or plate. The solid-phase extraction array or plate maycomprise a polar stationary phase material. The solid-phase extractionarray or plate may comprise a non-polar stationary phase material. Thesolid-phase extraction array or plate may comprise a C18 stationaryphase material (e.g., octadecyl group silica gel). The automatedapparatus may comprises a unit with a conditioning solution for thepurification unit (e.g., a conditioning solution for a solid-phaseextraction material). The automated apparatus may comprise a unit withan elution solution for removing biomolecules from the purificationunit.

In some embodiments, components are removed from a partition, except forthe plurality of sensor elements and a population of analytesinteracting with the plurality of sensor elements (i.e., a wash step).In some instances, the automated apparatus may perform a series of washsteps. A wash step may remove biomolecules that are not bound to thesensor elements within the partition. A wash step may desorb a subset ofbiomolecules bound to sensor elements within a partition. For example, awash step may result in the desorption and removal of a subset of softcorona analytes, while leaving the majority of hard corona analytesbound to the sensor element.

In some aspects, the present disclosure provides an automated apparatusto identify proteins in a biological sample, the automated apparatuscomprising: a sample preparation unit; a substrate comprising aplurality of channels; a plurality of pipettes; a plurality ofsolutions, a plurality of particles, and wherein the automated apparatusis configured to form a protein corona and digest the protein corona.

In some aspects, the present disclosure provides an automated apparatusto identify proteins in a biological sample, the automated apparatuscomprising: a sample preparation unit; a substrate comprising aplurality of channels; a plurality of pipettes; a plurality ofsolutions, a plurality of nanoparticles, wherein the automated apparatusis configured to form a protein corona and digest the protein corona,and wherein at least one of the solutions is TE 150 mM KCl 0.05% CHAPSbuffer.

In some embodiments, the sample preparation unit is configured to addthe plurality of nanoparticles to the substrate with the plurality ofpipettes. In some embodiments, wherein the sample preparation unit isconfigured to add the biological sample to the substrate with theplurality of pipettes. In some embodiments, the sample preparation unitis configured to incubate the plurality of nanoparticles and thebiological sample to form the protein corona.

In some embodiments, the sample preparation unit is configured toseparate the protein corona from the supernatant to form a proteincorona pellet. In some embodiments, the sample preparation unit isconfigured to reconstitute the protein corona pellet with TE 150 mM KCl0.05% CHAPS buffer.

In some embodiments, the automated apparatus further comprises amagnetic source. In some embodiments, the automated apparatus isconfigured for BCA, gel, or trypsin digestion of the protein corona.

In some embodiments, the automated apparatus is enclosed. In someembodiments, the automated apparatus is sterilized before use. In someembodiments, the automated apparatus is configured to a massspectrometry. In some embodiments, the automated apparatus istemperature controlled.

Assaying Methods

In some aspects, the present disclosure provides a method of identifyproteins in a biological sample. In some cases the method comprises:adding the biological sample to the automated apparatus disclosedherein; generating proteomic data from the automated apparatus; andquantifying the proteomic data.

In some embodiments, the method comprises incubating a plurality ofbiomolecules with the biological sample in the automated apparatus toform a biomolecule corona. In some embodiments, incubating thebiological sample with the plurality of sensor elements (e.g.,particles) contained within the partition of the substrate comprises anincubation time of at least about 10 seconds, at least about 15 seconds,at least about 20 seconds, at least about 25 seconds, at least about 30seconds, at least about 40 seconds, at least about 50 seconds, at leastabout 60 seconds, at least about 90 seconds, at least about 2 minutes,at least about 3 minutes, at least about 4 minutes, at least about 5minutes, at least about 6 minutes, at least about 7 minutes, at leastabout 8 minutes, at least about 9 minutes, at least about 10 minutes, atleast about 15 minutes, at least about 20 minutes, at least about 25minutes, at least about 30 minutes, at least about 45 minutes, at leastabout 50 minutes, at least about 60 minutes, at least about 90 minutes,at least about 2 hours, at least about 3 hours, at least about 4 hours,at least about 5 hours, at least about 6 hours, at least about 7 hours,at least about 8 hours, at least about 9 hours, at least about 10 hours,at least about 12 hours, at least about 14 hours, at least about 15hours, at least about 16 hours, at least about 17 hours, at least about18 hours, at least about 19 hours, at least about 20 hours, or at leastabout 24 hours. In some cases, two wells will have two differentincubation times. In some embodiments, incubating the biological samplewith the plurality of particles contained within the partition of thesubstrate comprises an incubation temperature between about 4° C. toabout 37° C. In some embodiments, incubating the biological sample withthe plurality of particles contained within the partition of thesubstrate comprises an incubation temperature between about 4° C. toabout 100° C.

The method, systems, and apparatus of the present disclosure maycomprise covering or sealing a partition on the substrate. This maycomprise covering a surface of the apparatus with a lid or a seal. Thelid or seal may prevent solutions or species from leaving a partition(e.g., evaporating from a partition). The automated apparatus may beconfigured to place and/or remove the lid or seal. The lid or seal maybe pierceable (e.g., may comprise a septum), thereby allowing a syringeor needle to enter a substrate partition without removal of the lid orseal.

In some cases, the system, apparatus and method of the presentdisclosure further comprise preparing analytes from the biomoleculecorona for analysis (e.g., mass spectrometric analysis). This cancomprise separating the biomolecule corona from the supernatant in theautomated apparatus. The biomolecule corona may be separated from thesupernatant by removing the supernatant and then desorbing a pluralityof proteins from the biomolecule corona into a desorbate solution (e.g.,a resuspension solution). In some cases, a first portion of biomoleculesfrom a biomolecule corona are desorbed from the biomolecule corona anddiscarded, and a second portion of biomolecules from a biomoleculecorona are desorbed from the biomolecule corona and collected (e.g., foranalysis). Multiple portions of biomolecules from a biomolecule coronamay be separately, desorbed, collected, and analyzed.

In some cases, biomolecules within a biomolecule corona are denatured,fragmented, chemically modified, or any combination thereof. Thesetreatments may be performed on desorbed biomolecules or on biomoleculecoronas. The plurality of biomolecules desorbed from a biomoleculecorona may comprise 1%, 2%, 3%, 4%, 5%, 6%, 8%, 10%, 12%, 15%, 20%, 25%,30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 98%, 99%, or greater than 99% ofthe biomolecules from the biomolecule corona. The desorption mayperformed for different lengths of time, including 5 seconds, 15seconds, 30 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5minutes, 6 minutes, 8 minutes, 10 minutes, 12 minutes, 15 minutes, 20minutes, 30 minutes, 40 minutes, 50 minutes, 1 hour, 1.5 hours, 2 hours,3 hours, 4 hours, 5 hours, 6 hours, 8 hours, 12 hours, or longer. Insome cases, the desorption comprises physical agitation, such as shakingor sonication. The percent of proteins desorbed from a particle coronamay depend on the desorption time, the chemical composition of thedesorbate solution into which proteins are desorbed (e.g., pH orbuffer-type), the desorption temperature, the form and intensity ofphysical agitation applied, or any combination thereof. Additionally,the types of proteins desorbed from a protein corona can be responsiveto desorption conditions and methods. The types of proteins desorbedfrom a protein corona may differ by 1%, 2%, 3%, 4%, 5%, 6%, 8%, 10%,12%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, or more between two desorptionconditions or methods.

In some cases, preparing analytes from a biomolecule corona for analysiscomprises digesting the biomolecule corona, a subset of biomoleculeswithin the protein corona, or biomolecules desorbed from the biomoleculecorona to form a digested sample in the automated apparatus. Preparinganalytes from the biomolecule corona for analysis may also comprisechemically modifying a biomolecule from the biomolecule corona, such asmethylating or reducing the biomolecule.

Desorbed biomolecules may be collected for further analysis (e.g., massspectrometric analysis). The automated apparatus may perform thecollecting, for example by collecting a volume of sample from asubstrate partition comprising biomolecules desorbed from biomoleculecoronas. A method may involve placing partition or plurality ofpartitions (e.g., a well plate) may be placed directly within aninstrument for performing said analysis.

A method may comprise multiple rounds of preparing analytes from abiomolecule corona for analysis. A method may comprise 1, 2, 3, 4, 5, 6,7, 8, 9, 10, or more rounds of preparation. In some cases, each roundproduces a separate sample for analysis (e.g., desorbed biomolecules maybe collected after each round and subjected to mass spectrometricanalysis). Two rounds may comprise desorbing different pluralities ofproteins from a biomolecule corona. Two rounds may also comprisedifferent desorption methods or conditions, such as different desorbatesolution volumes, different desorbate solution types (e.g., desorbatesolutions comprising different buffers or osmolarities), differenttemperatures, or different types and degrees of physical agitation. Twoor more successive rounds of preparation from a single biomoleculecorona (e.g., desorption and collection of a first subset ofbiomolecules from a biomolecule corona followed by desorption andcollection of a second subset of biomolecules from a biomolecule corona)may generate two sets of biomolecules. This may inform detection oranalysis of biomolecule interactions within a protein corona. As such,multiple rounds of preparation from a single biomolecule corona may beused to generate a number biomolecule subsets which exceed the number ofpartitions or types of sensor. For example, a method utilizing asubstrate with 96 partitions (e.g., a 96 well plate) may generate asmany as 960 unique biomolecule subsets if each partition comprises aunique combination of particles and solution conditions, and 10 roundsof analyte preparation are performed on each partition.

A different number of rounds of analyte preparation may be performed inseparate partitions. Partitions may also be subjected to differentanalyte preparation conditions. Performing more rounds of analytepreparation can increase the number of proteins or types of proteinscollected for analysis (e.g., generate more proteins that fall withinconcentration ranges accessible for simultaneous mass spectrometricdetection). The number proteins or types of proteins detected whenmultiple rounds of analyte preparation are performed may be 10%, 20%,30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, or more than 200%higher than if a single round of analyte preparation was performed.

In some cases, the method comprises immobilizing a sensor element withina partition. The immobilization may prevent the sensor element frombeing removed from the partition when a volume is removed from thepartition (e.g., the loading unit removes 95% of the solution from thepartition). Immobilization may be performed, for example, chemically(e.g., covalent or non-covalent binding to a substrate). Chemicalimmobilization may comprise reacting a sensor element with a surface ofthe partition. Chemical immobilization may also comprise non-covalentlyassociating a sensor element with a surface of the partition. Forexample, a sensor element may comprise biotin moieties that bind tostreptavidin bound to a surface of a partition. Immobilization may beachieved by applying a magnetic field to hold a magnetic sensor elementwithin a partition. For example, a plurality of sensor elements maycomprise a plurality of magnetic particles, and the substrate and themagnet may be in proximity such that the one or more magnetic particlesare immobilized within a partition in the substrate. Immobilization maybe achieved by providing a substrate with a sensor element formed orembedded within a partition on the substrate. For example, a sensorelement may be a half-particle formed on the surface of a partitionwithin the substrate.

In some cases, sensor element immobilization allows a biomolecule coronato be separated from the sensor element. This may comprise desorbing aplurality of biomolecules from a biomolecule corona associated with asensor element, immobilizing the sensor element within the partition,and then collecting the solution with the plurality of biomolecules fromthe biomolecule corona, thereby separating at least a portion of abiomolecule corona from a sensor element.

FIG. 4 illustrates examples of methods comprising immobilization sensorelements, which can be performed by the automated apparatus of thepresent disclosure. These methods utilize particles 402 and 411 tocapture a subset of biomolecules 403 and 404 in a sample.

Panel 400 shows a partition 401 containing particles 402 andbiomolecules. The particles are suspended within the partition, and haveadsorbed biomolecules 403 from the sample, thereby forming biomoleculecoronas. A number of biomolecules 404 may not adsorb to the particles,and will instead be suspended within the partition. Panel 410 shows analternative method, comprising particles 411 that are formed on thesurface of the partition.

Panels 420 and 430 show two methods for immobilizing the particles. Inpanel 420, the particles are collected onto the bottom of the partitionby a magnet 421. In panel 430, the particles are crosslinked to thepartition via linkers 431. Both methods result in the particles becomingimmobilized to the partition. Throughout the immobilization process,particle-adsorbed biomolecules 403 remain adsorbed to the particles,while the unbound biomolecules 404 remain unbound from the particles.

Panel 440 shows the results of wash steps on the partitions from panels410, 420, and 430. In all three cases, the wash removes unboundbiomolecules from the partition, while leaving the immobilized particlesand the biomolecules adsorbed to them. Panel 450 then shows desorptionof the biomolecule coronas, wherein a first plurality of biomolecules451 elute from the particles, and a second plurality of biomolecules 403remain adsorbed to the particles. The ratio of eluted to adsorbedbiomolecules and the types of biomolecules eluted from the particlesdepends on the elution conditions (e.g., temperature, degree and type ofphysical agitation, solution conditions such as pH). The elutedbiomolecules can be collected (e.g., by the loading unit) for furtherprocessing (e.g., fragmentation) or direct analysis.

FIG. 5 shows an example of a sample preparation method that can beperformed by the automated apparatus of the present disclosure. Thismethod utilizes sensor elements 512 to generate a subset of biomoleculesfrom a biological sample 502. The biological sample (shown in panel500), which is stored in a sample container 501, comprises a number ofbiomolecules. A volume of the sample can optionally be processed 504(e.g., cells within the sample can be lysed, nucleic acids and proteinscan be fragmented, the sample can be filtered to remove largebiomolecules, etc.), and then added to a partition 511 comprising sensorelements 512. As is depicted in panel 520 a portion of the biomolecules521 can bind to the sensor elements, separating them from a portion ofbiomolecules 522 that does not bind to the sensor elements. As is shownin panel 530, the sensor elements can then be immobilized within thepartition by bringing the partition in contact with a magnet 531. Thepartition can then undergo a wash cycle (e.g., addition of buffer to thepartition followed by removal of sample from the partition), resultingin the removal of the portion of biomolecules 522 that did not bind tothe sensor elements (shown in panel 540). The bound biomolecules 521 canbe eluted from the sensor elements and collected for further processingor analysis.

FIG. 6 illustrates a sample preparation method that can be performed bythe automated apparatus of the present disclosure. This method utilizessensor elements 512 that are formed on the surface of a substratepartition 511 to collect biomolecules 503 from a sample 502. Thebiological sample is transferred 504 from a sample holding unit 501 tothe substrate partition 511. As is shown in panel 520, the sensorelements will adsorb a first portion of the biomolecules from the sample521, while a second portion 522 will remain unbound. Panel 530 depictsthe removal of the unbound biomolecules, which leaves the sensorelements 512 and sensor element-bound biomolecules 521 within thepartition. These biomolecules can subsequently be desorbed from thesensor elements and collected (e.g., by the loading unit) for furtherprocessing or analysis.

The methods disclosed herein may comprise filtering a sensor elementfrom a solution. For example, the method may comprise desorbing aplurality of biomolecules from a biomolecule corona associated with asensor element, and filtering the solution such that the sensor elementis collected on the filter and the plurality of biomolecules remain insolution. The filtering may be performed after denaturation (e.g.,digestion). The filtering may also remove a plurality of biomolecules orbiological species such as intact proteins (e.g., undigested proteinsfrom the biological sample or proteases).

In some cases, the method comprises a purification step. Thepurification step can precede or follow preparation of analytes from abiomolecule corona. A purification step may comprise transferring abiological sample (e.g., biomolecules eluted and collected from abiomolecule corona) to a purification unit (e.g., a chromatographycolumn) or partition within a purification unit. Purification mayinvolve transferring a plurality of sample partitions from the substrateinto separate partitions in the purification unit. The purification unitmay comprise a solid-phase extraction plate. The purification step mayremove reagents (e.g., chemicals and enzymes) from the denaturationsolution. Following purification, the biological sample may berecollected for further enrichment or chemical treatment within thesubstrate or purification unit, or may be collected for direct analysis(e.g., mass spectrometric analysis).

Collectively, the methods of the present disclosure enable a high degreeof profiling depth for biological samples. The subset of biomoleculescollected in the methods of the present disclosure may enable, withoutfurther manipulation or modification of said subset of biomolecules,mass spectrometric detection of at least 2%, at least 3%, at least 4%,at least 5%, at least 6%, at least 7%, at least 8%, at least 9%, atleast 10%, at least 12%, at least 15%, at least 20%, at least 25%, atleast 30%, at least 40%, at least 50%, at least 60%, or more than 60% ofthe types of biomolecules in the biological sample from which the subsetof biomolecules were collected. The subset of biomolecules may enable,without further manipulation or modification of said subset ofbiomolecules, mass spectrometric detection of at least 2%, at least 3%,at least 4%, at least 5%, at least 6%, at least 7%, at least 8%, atleast 9%, at least 10%, at least 12%, at least 15%, at least 20%, atleast 25%, at least 30%, at least 40%, at least 50%, or more than 50% ofthe types of proteins in a sample. The subset of biomolecules collectedon a sensor element or prepared for analysis may enable, without furthermanipulation or modification of said subset of biomolecules,simultaneous mass spectrometric detection of two biomolecules (e.g.,proteins) spanning 6, 7, 8, 9, 10, 11, 12 or more orders of magnitude ina sample. For example, the two biomolecules may be desorbed andcollected at concentrations within 6 orders of magnitude within a singlesample, fragmented, and then submitted for mass spectrometric analysis.

In some cases, a type of sensor element (e.g., all sensor elements of agiven type that are within contact of a single sample) adsorbs at least100 to at least 300 types of proteins upon contacting a biologicalsample. A type of sensor element may adsorb at least 200 to at least 500types of proteins upon contacting a biological sample. A type of sensorelement may adsorb at least 300 to at least 800 types of proteins uponcontacting a biological sample. A type of sensor element may adsorb atleast 400 to at least 1000 types of proteins upon contacting abiological sample. A type of sensor element may adsorb at least 500 toat least 1200 types of proteins upon contacting a biological sample.

In some cases, the proteins collected from a plurality of sensorelements will be identified on the level of protein groups. Theplurality of protein groups collected on sensor elements in a partitionmay comprise from 1 to 20,000 protein groups. The plurality of proteingroups collected on sensor elements in a partition may comprise from 100to 10,000 protein groups. The plurality of protein groups collected onsensor elements in a partition may comprise from 100 to 5,000 proteingroups. The plurality of protein groups collected on sensor elements ina partition may comprise from 300 to 2,200 protein groups. The pluralityof protein groups collected on sensor elements in a partition maycomprise from 1,200 to 2,200 protein groups. The plurality of proteingroups collected on sensor elements in a partition may comprise from20,000 to 25,000 protein groups. The plurality of protein groupscollected on sensor elements in a partition may comprise from 25,000 to30,000 protein groups. The plurality of protein groups collected onsensor elements in a partition may comprise from 30,000 to 50,000protein groups.

The methods of the present disclosure can result in the enrichment oflow abundance biomolecules (e.g., proteins) from a biological sample. Alow abundance biomolecule may be a biomolecule at a concentration of 10ng/mL or less in a biological sample.

The methods of the present disclosure can result in the enrichment ofbiomolecules (e.g., proteins) present at a concentration that is atleast 6 orders of magnitude lower than the concentration of the mostabundant biomolecule of the same type in the same sample (e.g., a lowabundance protein may be a protein whose concentration is at least 6orders of magnitude lower than the most abundant protein in the sample).Databases, such as the Carr database (Keshishian et al., Mol. CellProteomics 14, 2375-2393 (2015), Plasma Proteome Database(plasmaproteomedatabase.org)) characterizing the plasma proteome, mayprovide a basis of comparison such that one can ascertain whether aprotein or biomolecule detected is enriched relative to otherbiomolecule(s) present in a plasma sample. Similar databases may be usedfor other types of biological samples.

In particular cases, the biological sample comprises blood, plasma, orserum, and a biomolecule corona comprises a lower proportion of albuminto non-albumin proteins than the biological sample. The ratio of albuminto non-albumin proteins may be 20%, 30%, 40%, 50%, 60%, or 70% lower ina biomolecule corona than in the sample from which proteins wereadsorbed.

The concentration range of a plurality of biomolecules may be compressedupon formation of a biomolecule corona. For example, the automatedapparatus may increase the number of types of biomolecules whoseconcentrations are within 6 orders of magnitude of the most concentratedbiomolecule in the sample by at least 25%, 50%, 100%, 200%, 300%, 500%,or 1000%. Analogously, the compressed dynamic range may comprise anincrease in the number of types of proteins whose concentrations arewithin 6 orders of magnitude of the most abundant biomolecule in thesample. The automated apparatus may increase the number of types ofproteins whose concentrations are within 6 orders of magnitude of themost concentrated protein in the sample by at least 25%, 50%, 100%,200%, 300%, 500%, or 1000%. The automated apparatus may enrich a subsetof biomolecules from a biological sample, and the subset of biomoleculesmay comprise at least 10% of the types of biomolecules from thebiological sample within a 6 order of magnitude concentration range. Theautomated apparatus may enrich a subset of biomolecules from abiological sample, and the subset of biomolecules may comprise at least20% of the types of biomolecules from the biological sample within a 6order of magnitude concentration range. The automated apparatus mayenrich a subset of biomolecules from a biological sample, and the subsetof biomolecules may comprise at least 30% of the types of biomoleculesfrom the biological sample within a 6 order of magnitude concentrationrange. The automated apparatus may enrich a subset of biomolecules froma biological sample, and the subset of biomolecules may comprise atleast 40% of the types of biomolecules from the biological sample withina 6 order of magnitude concentration range. The automated apparatus mayenrich a subset of biomolecules from a biological sample, and the subsetof biomolecules may comprise at least 50% of the types of biomoleculesfrom the biological sample within a 6 order of magnitude concentrationrange. The automated apparatus may enrich a subset of biomolecules froma biological sample, and the subset of biomolecules may comprise atleast 60% of the types of biomolecules from the biological sample withina 6 order of magnitude concentration range. The automated apparatus mayenrich a subset of biomolecules from a biological sample, and the subsetof biomolecules may comprise at least 70% of the types of biomoleculesfrom the biological sample within a 6 order of magnitude concentrationrange. The automated apparatus may enrich a subset of biomolecules froma biological sample, and the subset of biomolecules may comprise atleast 10% of the types of proteins from the biological sample within a 6order of magnitude concentration range. The automated apparatus mayenrich a subset of biomolecules from a biological sample, and the subsetof biomolecules may comprise at least 20% of the types of proteins fromthe biological sample within a 6 order of magnitude concentration range.The automated apparatus may enrich a subset of biomolecules from abiological sample, and the subset of biomolecules may comprise at least30% of the types of proteins from the biological sample within a 6 orderof magnitude concentration range. The automated apparatus may enrich asubset of biomolecules from a biological sample, and the subset ofbiomolecules may comprise at least 40% of the types of proteins from thebiological sample within a 6 order of magnitude concentration range. Theautomated apparatus may enrich a subset of biomolecules from abiological sample, and the subset of biomolecules may comprise at least50% of the types of proteins from the biological sample within a 6 orderof magnitude concentration range. The automated apparatus may enrich asubset of biomolecules from a biological sample, and the subset ofbiomolecules may comprise at least 60% of the types of proteins from thebiological sample within a 6 order of magnitude concentration range. Theautomated apparatus may enrich a subset of biomolecules from abiological sample, and the subset of biomolecules may comprise at least70% of the types of proteins from the biological sample within a 6 orderof magnitude concentration range.

The methods and sensor elements of the present disclosure may betailored so that biomolecule corona composition is invariant withrespect to sample lipid concentration. Changes of at most 10% in thelipid concentration in a biological sample may result in changes of lessthan 5%, 2%, 1%, or 0.1% in the composition of the proteins in abiomolecule corona. Changes of at most 10% in the lipid concentration ina biological sample may result in changes of less than 5%, 2%, 1%, or0.1% in the number of types of proteins in a biomolecule corona. Changesof at most 10% in the lipid concentration in a biological sample mayresult in changes of less than 5%, 2%, 1%, or 0.1% in the total numberof proteins in a biomolecule corona.

In some embodiments, the method further comprises washing the digestedsample in the automated apparatus. In some embodiments, quantifying theproteomic data comprises providing the proteomic data to a massspectrometer. In some embodiments, the biological sample is a biofluid.In some embodiments, the biofluid is serum or plasma.

In some cases, the entire assay time from a single sample, such as apooled plasma sample, including sample preparation and LC-MS, can beabout 8 hours. The entire assay time from a single sample, such as apooled plasma sample, including sample preparation and LC-MS, can beabout at least 1 hour, at least 2 hours, at least 3 hours, at least 4hours, at least 5 hours, at least 6 hours, at least 7 hours, at least 8hours, at least 9 hours, at least 10 hours, under 20 hours, under 19hours, under 18 hours, under 17 hours, under 16 hours, under 15 hours,under 14 hours, under 13 hours, under 12 hours, under 11 hours, under 10hours, under 9 hours, under 8 hours, under 7 hours, under 6 hours, under5 hours, under 4 hours, under 3 hours, under 2 hours, under 1 hour, atleast 5 min to 10 min, at least 10 min to 20 min, at least 20 min to 30min, at least 30 min to 40 min, at least 40 min to 50 min, at least 50min to 60 min, at least 1 hour to 1.5 hours, at least 1.5 hour to 2hours, at least 2 hour to 2.5 hours, at least 2.5 hour to 3 hours, atleast 3 hour to 3.5 hours, at least 3.5 hour to 4 hours, at least 4 hourto 4.5 hours, at least 4.5 hour to 5 hours, at least 5 hour to 5.5hours, at least 5.5 hour to 6 hours, at least 6 hour to 6.5 hours, atleast 6.5 hour to 7 hours, at least 7 hour to 7.5 hours, at least 7.5hour to 8 hours, at least 8 hour to 8.5 hours, at least 8.5 hour to 9hours, at least 9 hour to 9.5 hours, or at least 9.5 hour to 10 hours.

Dynamic Range

The biomolecule corona analysis methods described herein may compriseassaying biomolecules in a sample of the present disclosure across awide dynamic range. The dynamic range of biomolecules assayed in asample may be a range of measured signals of biomolecule abundances asmeasured by an assay method (e.g., mass spectrometry, chromatography,gel electrophoresis, spectroscopy, or immunoassays) for the biomoleculescontained within a sample. For example, an assay capable of detectingproteins across a wide dynamic range may be capable of detectingproteins of very low abundance to proteins of very high abundance. Thedynamic range of an assay may be directly related to the slope of assaysignal intensity as a function of biomolecule abundance. For example, anassay with a low dynamic range may have a low (but positive) slope ofthe assay signal intensity as a function of biomolecule abundance, e.g.,the ratio of the signal detected for a high abundance biomolecule to theratio of the signal detected for a low abundance biomolecule may belower for an assay with a low dynamic range than an assay with a highdynamic range. In specific cases, dynamic range may refer to the dynamicrange of proteins within a sample or assaying method.

The biomolecule corona analysis methods described herein may compressthe dynamic range of an assay. The dynamic range of an assay may becompressed relative to another assay if the slope of the assay signalintensity as a function of biomolecule abundance is lower than that ofthe other assay. For example, a plasma sample assayed using proteincorona analysis with mass spectrometry may have a compressed dynamicrange compared to a plasma sample assayed using mass spectrometry alone,directly on the sample or compared to provided abundance values forplasma proteins in databases (e.g., the database provided in Keshishianet al., Mol. Cell Proteomics 14, 2375-2393 (2015), also referred toherein as the “Carr database”), as shown in FIG. 13 and FIG. 14. Thecompressed dynamic range may enable the detection of more low abundancebiomolecules in a biological sample using biomolecule corona analysiswith mass spectrometry than using mass spectrometry alone.

In some embodiments, the dynamic range of a proteomic analysis assay maybe the ratio of the signal produced by highest abundance proteins (e.g.,the highest 10% of proteins by abundance) to the signal produced by thelowest abundance proteins (e.g., the lowest 10% of proteins byabundance). Compressing the dynamic range of a proteomic analysis maycomprise decreasing the ratio of the signal produced by the highestabundance proteins to the signal produced by the lowest abundanceproteins for a first proteomic analysis assay relative to that of asecond proteomic analysis assay. The protein corona analysis assaysdisclosed herein may compress the dynamic range relative to the dynamicrange of a total protein analysis method (e.g., mass spectrometry, gelelectrophoresis, or liquid chromatography).

Provided herein are several methods for compressing the dynamic range ofa biomolecular analysis assay to facilitate the detection of lowabundance biomolecules relative to high abundance biomolecules. Forexample, a particle type of the present disclosure can be used toserially interrogate a sample. Upon incubation of the particle type inthe sample, a biomolecule corona comprising forms on the surface of theparticle type. If biomolecules are directly detected in the samplewithout the use of said particle types, for example by direct massspectrometric analysis of the sample, the dynamic range may span a widerrange of concentrations, or more orders of magnitude, than if thebiomolecules are directed on the surface of the particle type. Thus,using the particle types disclosed herein may be used to compress thedynamic range of biomolecules in a sample. Without being limited bytheory, this effect may be observed due to more capture of higheraffinity, lower abundance biomolecules in the biomolecule corona of theparticle type and less capture of lower affinity, higher abundancebiomolecules in the biomolecule corona of the particle type.

A dynamic range of a proteomic analysis assay may be the slope of a plotof a protein signal measured by the proteomic analysis assay as afunction of total abundance of the protein in the sample. Compressingthe dynamic range may comprise decreasing the slope of the plot of aprotein signal measured by a proteomic analysis assay as a function oftotal abundance of the protein in the sample relative to the slope ofthe plot of a protein signal measured by a second proteomic analysisassay as a function of total abundance of the protein in the sample. Theprotein corona analysis assays disclosed herein may compress the dynamicrange relative to the dynamic range of a total protein analysis method(e.g., mass spectrometry, gel electrophoresis, or liquidchromatography).

Automated Systems

Various aspects of the present disclosure provide an automated systemcomprising an automated apparatus configured to isolate a subset ofbiomolecules from a biological sample, a mass spectrometer configured toreceive the subset of biomolecules and to generate data comprising massspectrometric or tandem mass spectrometric signals, and a computercomprising one or more computer processors and a computer readablemedium comprising machine-executable code that, upon execution of thecode, generates a biological fingerprint and assigns a biological statebased on the biological fingerprint.

In many cases, the automated apparatus comprises a sensor element orplurality of sensor elements which adsorb biomolecules from biologicalsolutions, thereby forming biomolecule coronas. They type, amount, andcategories of the biomolecules that make up these biomolecule coronasare strongly related to the physicochemical properties of the sensorelements themselves and the complex interactions between the differentbiomolecules themselves and the sensor elements. These interactions leadto the production of a unique biomolecule corona signature for eachsensor element. In other words, depending on which biomolecules interactwith the sensor element not only influences the makeup of thebiomolecule corona but also can alter which other different biomoleculescan also interact with that specific sensor element.

Different sensor elements each with their own biomolecule coronasignature can be contacted with a sample to produce a unique biomoleculefingerprint for that sample. This fingerprint can then be used todetermine a disease state of a subject. A plurality of sensor elementsmay be able to bind a plurality of biomolecules in a sample to produce abiomolecule corona signature. A plurality of sensor elements may havedistinct biomolecule corona signatures. In particular cases, each typeof sensor element has a distinct biomolecule corona signature. Forexample, a plurality of particles comprising 5 pM of each of 5 types ofparticles could have one biomolecule corona signature for each particletype.

The plurality of sensor elements when contacted with a sample produces aplurality of biomolecule corona signatures which together form abiomolecule fingerprint. The “biomolecule fingerprint” is the combinedcomposition or pattern of biomolecules of at least two biomoleculecorona signatures for the plurality of sensor elements. The biomoleculefingerprint may comprise at least 5, 10, 20, 40, 80, 150, or 200distinct biomolecule corona signatures.

In some cases, the automated system is configured so that thebiomolecule corona may be assayed separately for each sensor element,allowing the biomolecule corona signature to be determined for eachelement. More broadly, the automated system may be configured so thateach sample partition (e.g., each well in the substrate) can be assayedseparately, so that the combined set of biomolecule corona signaturesmay be determined for each partition.

Analogously, the computer may be configured to compare data frommultiple biomolecule corona signatures, partitions, or separate subsetsof biomolecules collected from an individual partition (e.g., throughmultiple rounds of desorption). This can provide a profiling sensitivitythat is not possible with conventional methods. Many biological states(such as many pre-disease states) create minute variance in a patient'sbiological sample (e.g., blood, urine, etc.) that are often notdiscernible from biomarker analysis alone. The power of the presentapparatuses, systems, methods, and sensor elements in part, stems fromthe interdependence of sensor element characteristics and biologicalsample composition on biomolecule corona composition, so that a smallchange in the populations, chemical states (e.g., post-translationalmodification status), or even conformations of sparsely populatedbiomolecules can have a major impact on the biomolecule corona signaturefor a particular sensor element. Furthermore, a biological state whichmay not be evident from a single set of data may be clearly elucidatedby the correlation between disparate biomolecule abundances acrossmultiple biomolecule corona signatures or sample partition measurements.Thus, a combination of nearly identical biomolecule corona signaturescan distinguish healthy subjects from cancer-ridden subjects with a highdegree of accuracy.

In some cases, the computer is configured to process the data comprisingthe intensity, APEX, spectral count or number of peptides, Ion mobilitybehavior of the mass spectrometric or tandem mass spectrometric signalbetween a plurality of the distinct biomolecule corona signatures. Thecomputer may be configured to process between 5,000 and 5,000,000signals between a plurality of the distinct biomolecule coronasignatures or sample partitions. The computer may be configured toprocess between 10,000 and 5,000,000 signals between a plurality of thedistinct biomolecule corona signatures or sample partitions. Thecomputer may be configured to compare between 20,000 and 200,000 signalsbetween a plurality of the distinct biomolecule corona signatures orsample partitions. The computer may be configured to compare between400,000 and 1,000,000 signals between a plurality of the distinctbiomolecule corona signatures or sample partitions. The computer may beconfigured to compare between 600,000 and 2,000,000 signals between aplurality of the distinct biomolecule corona signatures or samplepartitions. The computer may be configured to compare between 1,000,000and 5,000,000 signals between a plurality of the distinct biomoleculecorona signatures or sample partitions. In some cases, the signalscomprise mass spectrometric or tandem mass spectrometric signals.

An aspect of the present disclosure provides methods for generating abiomolecule fingerprint from one or more sets of mass spectrometricdata, tandem mass spectrometric data, chromatographic data, ion mobilitydata, or any combination thereof. In some cases, mass spectrometricdata, tandem mass spectrometric data, chromatographic data, or ionmobility data may be used to determine the concentration of abiomolecule from a biological sample. A plurality of sample partitionsmay be subjected to a separate mass spectrometric or tandem massspectrometric runs. A plurality of sample partitions may also be pooledand collectively analyzed in a single mass spectrometric or tandem massspectrometric run. Multiple mass spectrometric runs may be coupled withmultiple different chromatographic methods (e.g., different columns,buffers, or gradients). A single mass spectrometric or tandem massspectrometric run is performed in less than two hours, less than onehour, or less than half an hour.

Aspects of the present disclosure provide methods for identifyingbiological states and biomolecules with high degrees of certainty andaccuracy. The computer may be configured to identify a biomolecule orcharacterize an unidentified molecular feature based on a massspectrometric or tandem mass spectrometric signal and or ion mobilityand chromatographic behavior with a probability or certainty thresholdof at least 95%. The computer may associate a biomolecule fingerprintwith a biological state with at least 70% accuracy, at least 75%accuracy, at least 80% accuracy, at least 85% accuracy, at least 90%accuracy, at least 92% accuracy, at least 95% accuracy, at least 96%accuracy, at least 97% accuracy, at least 98% accuracy, at least 99%accuracy, or 100% accuracy. The computer may associate a biomoleculefingerprint with a biological state with at least 70% sensitivity, atleast 75% sensitivity, at least 80% sensitivity, at least 85%sensitivity, at least 90% sensitivity, at least 92% sensitivity, atleast 95% sensitivity, at least 96% sensitivity, at least 97%sensitivity, at least 98% sensitivity, at least 99% sensitivity, or 100%sensitivity. The computer may be capable of distinguishing between twobiological states associated with biological fingerprints that differ byless than 20%, 15%, 10%, or 8%, 5%, 3%, 2%, or 1%. In some aspects, abiomolecule identification is validated if a threshold level ofdiagnostic signals are detected. For example, if a threshold number ofthree uniquely assignable peptide fragment signals is provided forprotein group identification in a mass spectrometric assay, then twopeptide fragment signals corresponding to a particular protein groupwill not be counted.

Sensor Elements

As used herein, the term “sensor element” refers to elements that areable to bind to a plurality of biomolecules when in contact with asample and encompasses the term “particle”. The sensor element may be anelement from about 5 nanometers (nm) to about 50000 nm in at least onedirection. Suitable sensor elements include, for example, but notlimited to a sensor element from about 5 nm to about 50,000 nm in atleast one direction, including, about 5 nm to about 40000 nm,alternatively about 5 nm to about 30000 nm, alternatively about 5 nm toabout 20,000 nm, alternatively about 5 nm to about 10,000 nm,alternatively about 5 nm to about 5000 nm, alternatively about 5 nm toabout 1000 nm, alternatively about 5 nm to about 500 nm, alternativelyabout 5 nm to 50 nm, alternatively about 10 nm to 100 nm, alternativelyabout 20 nm to 200 nm, alternatively about 30 nm to 300 nm,alternatively about 40 nm to 400 nm, alternatively about 50 nm to 500nm, alternatively about 60 nm to 600 nm, alternatively about 70 nm to700 nm, alternatively about 80 nm to 800 nm, alternatively about 90 nmto 900 nm, alternatively about 100 nm to 1000 nm, alternatively about1000 nm to 10000 nm, alternatively about 10000 nm to 50000 nm and anycombination or amount in between (e.g. 5 nm, 10 nm, 15 nm, 20 nm, 25 nm,30 nm, 35 nm, 40 nm, 45 nm, 50 nm, 55 nm, 60 nm, 65 nm, 70 nm, 80 nm, 90nm, 100 nm, 125 nm, 150 nm, 175 nm, 200 nm, 225 nm, 250 nm, 275 nm, 300nm, 350 nm, 400 nm, 450 nm, 500 nm, 550 nm, 600 nm, 650 nm, 700 nm, 750nm, 800 nm, 850 nm, 900 nm, 1000 nm, 1200 nm, 1300 nm, 1400 nm, 1500 nm,1600 nm, 1700 nm, 1800 nm, 1900 nm, 2000 nm, 2500 nm, 3000 nm, 3500 nm,4000 nm, 4500 nm, 5000 nm, 5500 nm, 6000 nm, 6500 nm, 7000 nm, 7500 nm,8000 nm, 8500 nm, 9000 nm, 10000 nm, 11000 nm, 12000 nm, 13000 nm, 14000nm, 15000 nm, 16000 nm, 17000 nm, 18000 nm, 19000 nm, 20000 nm, 25000nm, 30000 nm, 35000 nm, 40000 nm, 45000 nm, 50000 nm and any number inbetween). A nanoscale sensor element refers to a sensor element that isless than 1 micron in at least one direction. Suitable examples ofranges of nanoscale sensor elements include, but are not limited to, forexample, elements from about 5 nm to about 1000 nm in one direction,including, from example, about 5 nm to about 500 nm, alternatively about5 nm to about 400 nm, alternatively about 5 nm to about 300 nm,alternatively about 5 nm to about 200 nm, alternatively about 5 nm toabout 100 nm, alternatively about 5 nm to about 50 nm, alternativelyabout 10 nm to about 1000 nm, alternatively about 10 nm to about 750 nm,alternatively about 10 nm to about 500 nm, alternatively about 10 nm toabout 250 nm, alternatively about 10 nm to about 200 nm, alternativelyabout 10 nm to about 100 nm, alternatively about 50 nm to about 1000 nm,alternatively about 50 nm to about 500 nm, alternatively about 50 nm toabout 250 nm, alternatively about 50 nm to about 200 nm, alternativelyabout 50 nm to about 100 nm, and any combinations, ranges or amountin-between (e.g. 5 nm, 10 nm, 15 nm, 20 nm, 25 nm, 30 nm, 35 nm, 40 nm,45 nm, 50 nm, 55 nm, 60 nm, 65 nm, 70 nm, 80 nm, 90 nm, 100 nm, 125 nm,150 nm, 175 nm, 200 nm, 225 nm, 250 nm, 275 nm, 300 nm, 350 nm, 400 nm,450 nm, 500 nm, 550 nm, 600 nm, 650 nm, 700 nm, 750 nm, 800 nm, 850 nm,900 nm, 1000 nm, etc.). In reference to the sensor arrays describedherein, the use of the term sensor element includes the use of ananoscale sensor element for the sensor element and associated methods.

The term “plurality of sensor elements” refers to more than one, forexample, at least two sensor elements. In some embodiments, theplurality of sensor elements includes at least two sensor elements to atleast 10¹⁵ sensor elements. In some embodiments, the plurality of sensorelements includes 10⁶-10⁷, 10⁶-10⁸, 10⁶-10⁹, 10⁶-10¹⁰, 10⁶-10¹¹,10⁶-10¹², 10⁶-10¹¹, 10⁶-10¹⁴, 10⁶-10¹⁵, 10⁷-10⁸, 10⁷-10⁹, 10⁷-10¹⁰,10⁷-10¹¹, 10⁷-10¹², 10⁷-10¹¹, 10⁷-10¹⁴, 10⁷-10¹⁵, 10⁸-10⁹, 10⁸-10¹°,10⁸-10¹¹, 10⁸-10¹², 10⁸-10¹¹, 10⁸-10¹⁴, 10⁸-10¹⁵, 10⁹-10¹⁰, 10⁹-10¹¹,10⁹-10¹², 10⁹-10¹¹, 10⁹-10¹⁴, 10⁹-10¹⁵, 10¹⁰-10¹¹, 10¹⁰-10¹², 10¹⁰-10¹¹,10¹⁰-10¹⁴, 10¹⁰-10¹⁵, 10¹¹-10¹², 10¹¹-10¹¹, 10¹¹-10¹⁴, 10¹¹-10¹⁵,10¹²-10¹¹, 10¹²-10¹⁴, 10¹²-10¹⁵, 10¹³-10¹⁴, 10¹³-10¹⁵, or 10¹⁴-10¹⁵different sensor elements.

In some embodiments, a plurality of sensor elements comprises aplurality of types of sensor elements. A plurality of sensor elementsmay comprise at least two to at least 1000 types of sensor elements,alternatively at least two to at least 50 types of sensor elements,alternatively at least 2 to 30 types of sensor elements, alternativelyat least 2 to 20 types of sensor elements, alternatively at least 2 to10 types of sensor elements, alternatively at least 3 to at least 50types of sensor elements, alternatively at least 3 to at least 30 typesof sensor elements, alternatively at least 3 to at least 20 types ofsensor elements, alternatively at least 3 to at least 10 types of sensorelements, alternatively at least 4 to at least 50 types of sensorelements, alternatively at least 4 to at least 30 types of sensorelements, alternatively at least 4 to at least 20 types of sensorelements, alternatively at least 4 to at least 10 types of sensorelements, and including any number of types of sensor elementscontemplated in between (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,48, 49, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,190, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750,800, etc.). The plurality of sensor elements may comprise at least 6types of sensor elements to at least 20 types of sensor elements, oralternatively at least 6 types of sensor elements to at least 10 typesof sensor elements.

In some cases, increasing the number of sensor elements can be a methodfor increasing the number of biomolecules (e.g., proteins) that can beidentified in a given sample. An example of how increasing panel sizemay increase the number of identified proteins is shown in FIG. 10 .This figure shows the number of proteins identified from corona analysisin assays utilizing panels with 1 to 12 particle types. In these assays,distinct proteins, as opposed to protein groups, were identified throughmass spectrometric analysis. The number of types of proteins identifiedincreased with increasing number of particle types, spanning from 419unique identified proteins when one particle type was used to collectproteins, to 1318 unique identified proteins for when 12 types ofparticles were used to collect proteins.

The sensor elements may be functionalized to have a wide range ofphysicochemical properties. Suitable methods of functionalizing thesensor elements are known in the art and depend on composition of thesensor element (e.g. gold, iron oxide, silica, silver, etc.), andinclude, but are not limited to, for example aminopropyl functionalized,amine functionalized, boronic acid functionalized, carboxylic acidfunctionalized, methyl functionalized, succinimidyl esterfunctionalized, PEG functionalized, streptavidin functionalized, methylether functionalized, triethoxylpropylaminosilane functionalized, thiolfunctionalized, PCP functionalized, citrate functionalized, lipoic acidfunctionalized, BPEI functionalized, carboxyl functionalized, hydroxylfunctionalized, and the like. In one embodiment, the sensor elements maybe functionalized with an amine group (—NH₂ or a carboxyl group (COOH).In some embodiments, the nanoscale sensor elements are functionalizedwith a polar functional group. Non-limiting examples of the polarfunctional group comprise carboxyl group, a hydroxyl group, a thiolgroup, a cyano group, a nitro group, an ammonium group, an imidazoliumgroup, a sulfonium group, a pyridinium group, a pyrrolidinium group, aphosphonium group or any combination thereof. In some embodiments, thefunctional group is an acidic functional group (e.g., sulfonic acidgroup, carboxyl group, and the like), a basic functional group (e.g.,amino group, cyclic secondary amino group (such as pyrrolidyl group andpiperidyl group), pyridyl group, imidazole group, guanidine group,etc.), a carbamoyl group, a hydroxyl group, an aldehyde group and thelike. In some embodiments, the polar functional group is an ionicfunctional group. Non-limiting examples of the ionic function groupcomprise an ammonium group, an imidazolium group, a sulfonium group, apyridinium group, a pyrrolidinium group, a phosphonium group. In someembodiments, the sensor elements are functionalized with a polymerizablefunctional group. Non-limiting examples of the polymerizable functionalgroup include a vinyl group and a (meth)acrylic group. In someembodiments, the functional group is pyrrolidyl acrylate, acrylic acid,methacrylic acid, acrylamide, 2-(dimethylamino)ethyl methacrylate,hydroxyethyl methacrylate and the like.

The physicochemical properties of the sensor elements may be modified bymodification of the surface charge. For example, the surface can bemodified to provide a net neutral charge, a net positive surface charge,a net negative surface charge, or a zwitterionic charge. The charge ofthe surface can be controlled either during synthesis of the element orby post-synthesis modification of the charge through surfacefunctionalization. For polymeric sensor elements (e.g., polymericparticles), differences in charge can be obtained during synthesis byusing different synthesis procedures, different charged comonomers, andin inorganic substances by having mixed oxidation states.

Non-limiting examples of the plurality of sensor elements include, butare not limited to, (a) a plurality of sensor elements made of the samematerial but differing in physiochemical properties, (b) a plurality ofsensor elements where one or more sensor element is made of a differentmaterial with the same or differing physiochemical properties, (c) aplurality of sensor elements made of the same material differing insize, (d) a plurality of sensor elements made of different material withrelatively the same size; (e) a plurality of sensor elements made ofdifferent material and made of different sizes, (f) a plurality ofsensor elements in which each element is made of a different material,(g) a plurality of sensor elements having different charges, amongothers. The plurality of sensor elements can be in any suitablecombination of two or more sensor elements in which each sensor elementprovides a unique biomolecule corona signature. For example, theplurality of sensor elements may include one or more liposomes and oneor more particles described herein. In one embodiment, the plurality ofsensor elements can be a plurality of liposomes with varying lipidcontent and/or varying charges (cationic/anionic/neutral). In anotherembodiment, the plurality of sensors may contain one or morenanoparticle made of the same material but of varying sizes andphysiochemical properties. In another embodiment, the plurality ofsensors may contain one or more particle made of differing materials(e.g. silica and polystyrene) with similar or varying sizes and/orphysiochemical properties (e.g. modifications, for example, —NH₂, —COOHfunctionalization). These combinations are purely provided as examplesand are non-limiting to the scope of the disclosure.

A sensor element may comprise a particle, such as a nanoparticle or amicroparticle. A sensor element may be a particle, such as ananoparticle or a microparticle. A sensor element may comprise a surfaceor a portion of a surface of a material. A sensor element may comprise aporous material (e.g., a polymer matrix) into which biomolecules canintercalate. A sensor element may comprise a material with projections,such as polymers, oligomers, or metal dendrites. A sensor element maycomprise an aggregate of particles, such as a nanoworm.

Particle Materials

A plurality of particles disclosed herein can be made of a variety ofdifferent materials. A plurality of particles can comprise specifictypes of nanoparticles to identify a broad range of proteins in thesample, or to selectively assay for a particular protein or set ofproteins of interest.

A plurality of particles may comprise at least 1 particle distinct type,at least 2 distinct particle types, at least 3 distinct particle types,at least 4 distinct particle types, at least 5 distinct particle types,at least 6 distinct particle types, at least 7 distinct particle types,at least 8 distinct particle types, at least 9 distinct particle types,at least 10 distinct particle types, at least 11 distinct particletypes, at least 12 distinct particle types, at least 13 distinctparticle types, at least 14 distinct particle types, at least 15distinct particle types, at least 16 distinct particle types, at least17 distinct particle types, at least 18 distinct particle types, atleast 19 distinct particle types, at least 20 distinct particle types,at least 25 distinct particle types, at least 30 distinct particletypes, at least 35 distinct particle types, at least 40 distinctparticle types, at least 45 distinct particle types, at least 50distinct particle types, at least 55 distinct particle types, at least60 distinct particle types, at least 65 distinct particle types, atleast 70 distinct particle types, at least 75 distinct particle types,at least 80 distinct particle types, at least 85 distinct particletypes, at least 90 distinct particle types, at least 95 distinctparticle types, at least 100 distinct particle types, from 1 to 5distinct particle types, from 5 to 10 distinct particle types, from 10to 15 distinct particle types, from 15 to 20 distinct particle types,from 20 to 25 distinct particle types, from 25 to 30 distinct particletypes, from 30 to 35 distinct particle types, from 35 to 40 distinctparticle types, from 40 to 45 distinct particle types, from 45 to 50distinct particle types, from 50 to 55 distinct particle types, from 55to 60 distinct particle types, from 60 to 65 distinct particle types,from 65 to 70 distinct particle types, from 70 to 75 distinct particletypes, from 75 to 80 distinct particle types, from 80 to 85 distinctparticle types, from 85 to 90 distinct particle types, from 90 to 95distinct particle types, from 95 to 100 distinct particle types, from 1to 100 distinct particle types, from 20 to 40 distinct particle types,from 5 to 10 distinct particle types, from 3 to 7 distinct particletypes, from 2 to 10 distinct particle types, from 6 to 15 distinctparticle types, or from 10 to 20 distinct particle types. A plurality ofparticles may comprise from 3 to 10 particle types. A plurality ofparticles may comprise from 4 to 11 distinct particle types. A pluralityof particles may comprise from 5 to 15 distinct particle types. Aplurality of particles may comprise from 5 to 15 distinct particletypes. A plurality of particles may comprise from 8 to 12 distinctparticle types. A plurality of particles may comprise from 9 to 13distinct particle types. A plurality of particles may comprise 10distinct particle types. The particle types may include nanoparticles.

For example, the present disclosure a plurality of particles having atleast 2 distinct particle types, at least 3 different surfacechemistries, at least 4 different surface chemistries, at least 5different surface chemistries, at least 6 different surface chemistries,at least 7 different surface chemistries, at least 8 different surfacechemistries, at least 9 different surface chemistries, at least 10different surface chemistries, at least 11 different surfacechemistries, at least 12 different surface chemistries, at least 13different surface chemistries, at least 14 different surfacechemistries, at least 15 different surface chemistries, at least 20different surface chemistries, at least 25 different surfacechemistries, at least 30 different surface chemistries, at least 35different surface chemistries, at least 40 different surfacechemistries, at least 45 different surface chemistries, at least 50different surface chemistries, at least 100 different surfacechemistries, at least 150 different surface chemistries, at least 200different surface chemistries, at least 250 different surfacechemistries, at least 300 different surface chemistries, at least 350different surface chemistries, at least 400 different surfacechemistries, at least 450 different surface chemistries, at least 500different surface chemistries, from 2 to 500 different surfacechemistries, from 2 to 5 different surface chemistries, from 5 to 10different surface chemistries, from 10 to 15 different surfacechemistries, from 15 to 20 different surface chemistries, from 20 to 40different surface chemistries, from 40 to 60 different surfacechemistries, from 60 to 80 different surface chemistries, from 80 to 100different surface chemistries, from 100 to 500 different surfacechemistries, from 4 to 15 different surface chemistries, or from 2 to 20different surface chemistries.

The present disclosure provides a plurality of particles having at least2 different physical properties, at least 3 different physicalproperties, at least 4 different physical properties, at least 5different physical properties, at least 6 different physical properties,at least 7 different physical properties, at least 8 different physicalproperties, at least 9 different physical properties, at least 10different physical properties, at least 11 different physicalproperties, at least 12 different physical properties, at least 13different physical properties, at least 14 different physicalproperties, at least 15 different physical properties, at least 20different physical properties, at least 25 different physicalproperties, at least 30 different physical properties, at least 35different physical properties, at least 40 different physicalproperties, at least 45 different physical properties, at least 50different physical properties, at least 100 different physicalproperties, at least 150 different physical properties, at least 200different physical properties, at least 250 different physicalproperties, at least 300 different physical properties, at least 350different physical properties, at least 400 different physicalproperties, at least 450 different physical’ properties, at least 500different physical properties, from 2 to 500 different physicalproperties, from 2 to 5 different physical properties, from 5 to 10different physical properties, from 10 to 15 different physicalproperties, from 15 to 20 different physical properties, from 20 to 40different physical properties, from 40 to 60 different physicalproperties, from 60 to 80 different physical properties, from 80 to 100different physical properties, from 100 to 500 different physicalproperties, from 4 to 15 different physical properties, or from 2 to 20different physical properties.

Particles can be made from various materials. For example, nanoparticlematerials consistent with the present disclosure include metals,polymers, magnetic materials, and lipids. Magnetic nanoparticles may beiron oxide nanoparticles. Examples of metal materials include any one ofor any combination of gold, silver, copper, nickel, cobalt, palladium,platinum, iridium, osmium, rhodium, ruthenium, rhenium, vanadium,chromium, manganese, niobium, molybdenum, tungsten, tantalum, iron andcadmium, or any other material described in U.S. Pat. No. 7,749,299.

Examples of polymers include any one of or any combination ofpolyethylenes, polycarbonates, polyanhydrides, polyhydroxyacids,polypropylfumerates, polycaprolactones, polyamides, polyacetals,polyethers, polyesters, poly(orthoesters), polycyanoacrylates, polyvinylalcohols, polyurethanes, polyphosphazenes, polyacrylates,polymethacrylates, polycyanoacrylates, polyureas, polystyrenes, orpolyamines, a polyalkylene glycol (e.g., polyethylene glycol (PEG)), apolyester (e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, orpolycaprolactone), or a copolymer of two or more polymers, such as acopolymer of a polyalkylene glycol (e.g., PEG) and a polyester (e.g.,PLGA). In some embodiments, the polymer is a lipid-terminatedpolyalkylene glycol and a polyester, or any other material disclosed inU.S. Pat. No. 9,549,901. A polymer may also be a liposome.

Examples of lipids that can be used to form the nanoparticles of thepresent disclosure include cationic, anionic, and neutrally chargedlipids. For example, nanoparticles can be made of any one of or anycombination of dioleoylphosphatidylglycerol (DOPG),diacylphosphatidylcholine, diacylphosphatidylethanolamine, ceramide,sphingomyelin, cephalin, cholesterol, cerebrosides and diacylglycerols,dioleoylphosphatidylcholine (DOPC), dimyristoylphosphatidylcholine(DMPC), and dioleoylphosphatidylserine (DOPS), phosphatidylglycerol,cardiolipin, diacylphosphatidylserine, diacylphosphatidic acid,N-dodecanoyl phosphatidylethanolamines, N-succinylphosphatidylethanolamines, N-glutarylphosphatidylethanolamines,lysylphosphatidylglycerols, palmitoyloleyolphosphatidylglycerol (POPG),lecithin, lysolecithin, phosphatidylethanolamine,lysophosphatidylethanolamine, dioleoylphosphatidylethanolamine (DOPE),dipalmitoyl phosphatidyl ethanolamine (DPPE),dimyristoylphosphoethanolamine (DMPE),distearoyl-phosphatidyl-ethanolamine (DSPE),palmitoyloleoyl-phosphatidylethanolamine (POPE)palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine(EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidylcholine(DOPC), dipalmitoylphosphatidylcholine (DPPC),dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol(DPPG), palmitoyloleyolphosphatidylglycerol (POPG), 16-O-monomethyl PE,16-O-dimethyl PE, 18-1-trans PE,palmitoyloleoyl-phosphatidylethanolamine (POPE),1-stearoyl-2-oleoyl-phosphatidyethanolamine (SOPE), phosphatidylserine,phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidicacid, cerebrosides, dicetylphosphate, and cholesterol, or any othermaterial listed in U.S. Pat. No. 9,445,994.

In various cases, the core of the nanoparticles can include an organicparticle, an inorganic particle, or a particle including both organicand inorganic materials. For example, the particles can have a corestructure that is or includes a metal particle, a quantum dot particle,a metal oxide particle, or a core-shell particle. For example, the corestructure can be or include a polymeric particle or a lipid-basedparticle, and the linkers can include a lipid, a surfactant, a polymer,a hydrocarbon chain, or an amphiphilic polymer. For example, the linkerscan include polyethylene glycol or polyalkylene glycol, e.g., the firstends of the linkers can include a lipid bound to polyethelene glycol(PEG) and the second ends can include functional groups bound to thePEG. A particle may have a core-shell structure. In some cases, aparticle has a core comprising a first material or composite and aplurality of shells comprising different materials or composites. Insome cases, a particle has a magnetic core surrounded by a non-magneticor plurality of non-magnetic shells. For example, a particle maycomprise a magnetic iron oxide core surrounded by a non-magnetic polymershell. In some cases, magnetic core has a 10 nm to 500 nm diameter, andthe shell has a 5 nm to 100 nm thickness.

Examples of particle types consistent with the present disclosure areshown in TABLE 1 below. Additional examples of particles, such asmagnetic core nanoparticles (MNP) and corresponding surface chemistriesare illustrated in FIG. 7 .

TABLE 1 Particle Types P# Description Vendor HX-13 Carboxylate (Citrate)Seer or S-001 HX-19 Phenol-formaldehyde coated Seer or S-002 HX-31Polystyrene coated Seer or S-004 HX-38 Polystyrene/carboxylate coatedSeer or S-005 HX-42 Silica coated, amine Seer or S-006 HX-57 Benzoicacid Seer or S-008 HX-58 PVBTMAC coated Seer or S-009(Vinylbenzyltrimethylammonium chloride) HX-59 Carboxylate, PAA coatedSeer or S-010 P-033 Carboxylate Polysciences P-039 Polystyrene CarboxylMicro Particles P-041 Carboxylic acid OceanNanoTech P-047 SilicaOceanNanoTech P-048 Carboxylic acid OceanNanoTech P-053 Amino SpherotechP-056 Silica Amino Spherotech P-063 Jeffamine Spherotech P-064Polystyrene Spherotech P-065 Silica Spherotech P-069 Original coatingOceanNanoTech P-073 Dextran based Kisker Biotech P-074 Silica SilanolKisker Biotech HX-20 Silica-coated superparamagnetic Seer or S-003 ironoxide NPs (SPION) HX-56 poly(N-(3-(dimethylamino)propyl) Seer or S-007methacrylamide) (PDMAPMA)-coated SPION HX-86 poly(oligo(ethylene glycol)Seer or S-011 methyl ether methacrylate) (POEGMA)-coated SPION

Properties of Particles

Nanoparticles that are consistent with the present disclosure can bemade and used in methods of forming protein coronas after incubation ina biofluid at a wide range of sizes. For example, the nanoparticlesdisclosed herein can be at least 10 nm, at least 100 nm, at least 200nm, at least 300 nm, at least 400 nm, at least 500 nm, at least 600 nm,at least 700 nm, at least 800 nm, at least 900 nm, from 10 nm to 50 nm,from 50 nm to 100 nm, from 100 nm to 150 nm, from 150 nm to 200 nm, from200 nm to 250 nm, from 250 nm to 300 nm, from 300 nm to 350 nm, from 350nm to 400 nm, from 400 nm to 450 nm, from 450 nm to 500 nm, from 500 nmto 550 nm, from 550 nm to 600 nm, from 600 nm to 650 nm, from 650 nm to700 nm, from 700 nm to 750 nm, from 750 nm to 800 nm, from 800 nm to 850nm, from 850 nm to 900 nm, from 100 nm to 300 nm, from 150 nm to 350 nm,from 200 nm to 400 nm, from 250 nm to 450 nm, from 300 nm to 500 nm,from 350 nm to 550 nm, from 400 nm to 600 nm, from 450 nm to 650 nm,from 500 nm to 700 nm, from 550 nm to 750 nm, from 600 nm to 800 nm,from 650 nm to 850 nm, from 700 nm to 900 nm, or from 10 nm to 900 nm.

Additionally, particles can have a homogenous size distribution or aheterogeneous size distribution. Polydispersity index (PDI), which canbe measured by techniques such as dynamic light scattering, is a measureof the size distribution. A low PDI indicates a more homogeneous sizedistribution and a higher PDI indicates a more heterogeneous sizedistribution. In some cases, a plurality of particles has a PDI of 0.01to 0.1, 0.1 to 0.5, 0.5 to 1, 1 to 5, 5 to 20, or greater than 20.

Particles disclosed herein can have a range of different surfacecharges. Particles can be negatively charged, positively charged, orneutral in charge. In some embodiments, particles have a surface chargeof −500 mV to −450 mV, −450 mV to −400 mV, −400 mV to −350 mV, −350 mVto −300 mV, −300 mV to −250 mV, −250 mV to −200 mV, −200 mV to −150 mV,−150 mV to −100 mV, −100 mV to −90 mV, −90 mV to −80 mV, −80 mV to −70mV, −70 mV to −60 mV, −60 mV to −50 mV, −50 mV to −40 mV, −40 mV to −30mV, −30 mV to −20 mV, −20 mV to −10 mV, −10 mV to 0 mV, 0 mV to 10 mV,10 mV to 20 mV, 20 mV to 30 mV, 30 mV to 40 mV, 40 mV to 50 mV, 50 mV to60 mV, 60 mV to 70 mV, 70 mV to 80 mV, 80 mV to 90 mV, 90 mV to 100 mV,100 mV to 110 mV, 110 mV to 120 mV, 120 mV to 130 mV, 130 mV to 140 mV,140 mV to 150 mV, 150 mV to 200 mV, 200 mV to 250 mV, 250 mV to 300 mV,300 mV to 350 mV, 350 mV to 400 mV, 400 mV to 450 mV, 450 mV to 500 mV,−500 mv to −400 mV, −400 mv to −300 mV, −300 mv to −200 mV, −200 my to−100 mV, −100 mv to 0 mV, 0 mv to 100 mV, 100 mv to 200 mV, 200 mv to300 mV, 300 mv to 400 mV, or 400 mv to 500 mV.

Various particle morphologies are consistent with the particle types inpanels of the present disclosure. For example, particles may bespherical, colloidal, square shaped, rods, wires, cones, pyramids, oroblong.

Biomolecule Coronas

Provided herein are automated apparatuses, systems, methods, and sensorelements capable of generating biomolecule coronas comprising,consisting essentially of or consisting of a plurality of sensorelements wherein the plurality of sensor elements differ from each otherin at least one physicochemical property. The plurality of sensorelements may comprise a plurality of particles (e.g., nanoparticles).The plurality of sensor elements may be a plurality of particles. Aplurality of sensor elements may be able to bind a plurality ofbiomolecules in a complex biological sample to produce a biomoleculecorona signature. A plurality of sensor elements may comprise aplurality of distinct biomolecule corona signatures.

A biomolecule of interest (e.g., a low abundance protein) may beenriched in a biomolecule corona relative to the untreated sample (e.g.,a sample that is not assayed using particles). The biomolecule ofinterest may be a protein. The biomolecule corona may be a proteincorona. A level of enrichment may be the percent increase or foldincrease in relative abundance of the biomolecule of interest (e.g.,number of copies of the biomolecule of interest versus the total numberof biomolecules) in the biomolecule corona as compared to the biologicalsample from which the biomolecule corona was collected. A biomolecule ofinterest may be enriched in a biomolecule corona by increasing theabundance of the biomolecule of interest in the biomolecule corona ascompared to the sample that has not been contacted to the sensorelement. A biomolecule of interest may be enriched by decreasing theabundance of a biomolecule that is in high abundance biological sample.

A biomolecule corona analysis assay may be used to rapidly identify lowabundance biomolecules in a biological sample (e.g., a biofluid).Biomolecule corona analysis may be used to identify at least about 500low abundance biomolecules in a biological sample in no more than about8 hours from first contacting the biological sample with a sensorelement (e.g., a particle). Biomolecule corona analysis may identify atleast about 1000 low abundance biomolecules in a biological sample in nomore than about 8 hours from first contacting the biological sample witha sensor element. Biomolecule corona analysis may identify at leastabout 500 low abundance biomolecules in a biological sample in no morethan about 4 hours from first contacting the biological sample with asensor element. Biomolecule corona analysis may identify at least about1000 low abundance biomolecules in a biological sample in no more thanabout 4 hours from first contacting the biological sample with a sensorelement.

A biomolecule corona signature may comprise a protein, a peptide, apolysaccharide, an oligosaccharide, a monosaccharide, a metabolite, alipid, a nucleic acid, or any combination thereof. The biomoleculecorona signature may be a protein corona signature. The biomoleculecorona signature may be a polysaccharide corona signature. Thebiomolecule corona signature may be a metabolite corona signature. Thebiomolecule corona signature may be a lipidomic corona signature. Thebiomolecule corona signature may comprise the biomolecules found in asoft corona and a hard corona. The soft corona may be a soft proteincorona. The hard corona may be a hard protein corona.

The biomolecule corona signature refers to the composition, signature orpattern of different biomolecules that are bound to each separate sensorelement or each nanoparticle. In some cases, the biomolecule coronasignature is a protein corona signature. In another case, thebiomolecule corona signature is a polysaccharide corona signature. Inyet another case, the biomolecule corona signature is a metabolitecorona signature. In some cases, the biomolecule corona signature is alipidomic corona signature. The signature can refer to the differentbiomolecules. It can also refer to the differences in the amount, levelor quantity of the biomolecule bound to the sensor element or thenanoparticle, or differences in the conformational state of thebiomolecule that is bound to the sensor element or the particle. It iscontemplated that the biomolecule corona signatures of each sensorelements may contain some of the same biomolecules, may contain distinctbiomolecules with regard to the other sensor elements or nanoparticles,and/or may differ in level or quantity, type or confirmation of thebiomolecule. The biomolecule corona signature may depend on not only thephysicochemical properties of the sensor element or the particle, butalso the nature of the sample and the duration of exposure. In someembodiments, the biomolecule corona signature comprises the biomoleculesfound in a soft corona and a hard corona.

In some embodiments, a plurality of sensor elements includes a firstsensor element that produces a first biomolecule corona signature and atleast one second sensor element (e.g., at least one nanoparticle) thatproduces at least one second biomolecule corona signature when thesensor array is contacted with a complex biological sample. In somecases, each type of sensor element from among a plurality of sensorelements produces a different biomolecule corona signature.

The plurality of sensor elements when contacted with a sample produces aplurality of biomolecule corona signatures which together can form abiomolecule fingerprint. The “biomolecule fingerprint” refers to thecombined composition or pattern of biomolecules of at least twobiomolecule corona signatures for the plurality of sensor elements. Itis contemplated that the biomolecule fingerprint can be made from atleast two biomolecule corona signatures to as many different biomoleculesignatures are assayed, e.g. at least 1000 different biomolecule coronasignatures. The biomolecule corona can be assayed separately for eachsensor element to determine the biomolecule corona signature for eachsensor element (e.g., each nanoparticle or each liposome) and combinedto form the biomolecule fingerprint. In some cases, the biomoleculefingerprint can be developed by assaying the two or more biomoleculecoronas simultaneously.

Identified Proteins

The automated apparatuses, systems, methods, and sensor elements (e.g.,particles) disclosed herein can be used to identify a number ofbiomolecules, proteins, peptides, or protein groups. Featureintensities, as disclosed herein, refers to the intensity of a signalfrom an analytical measurement, for example the intensity of a mass tocharge ratio from a mass spectrometry run of a sample. Using the dataanalysis methods described herein, feature intensities of peptides andpeptide fragments can be sorted into protein groups. Protein groupsrefer to two or more proteins that are identified by a shared peptidesequence. Alternatively, a protein group can refer to one protein thatis identified using a unique identifying sequence. For example, if in asample, a peptide sequence is assayed that is shared between twoproteins (Protein 1: XYZZX and Protein 2: XYZYZ), a protein group couldbe the “XYZ protein group” having two members (protein 1 and protein 2).Alternatively, if the peptide sequence is unique to a single protein(Protein 1), a protein group could be the “ZZX” protein group having onemember (Protein 1). Each protein group can be supported by more than onepeptide sequence. Protein detected or identified according to theinstant disclosure can refer to a distinct protein detected in thesample (e.g., distinct relative other proteins detected using massspectrometry). Thus, analysis of proteins present in distinct coronascorresponding to the distinct sensor element types yields a high numberof feature intensities. This number decreases as feature intensities areprocessed into distinct peptides, further decreases as distinct peptidesare processed into distinct proteins, and further decreases as peptidesare grouped into protein groups (two or more proteins that share adistinct peptide sequence).

The automated apparatuses, systems, methods, and sensor elements (e.g.,particles) disclosed herein can be used to identify at least at least100 protein groups, at least 200 protein groups, at least 300 proteingroups, at least 400 protein groups, at least 500 protein groups, atleast 600 protein groups, at least 700 protein groups, at least 800protein groups, at least 900 protein groups, at least 1000 proteingroups, at least 1100 protein groups, at least 1200 protein groups, atleast 1300 protein groups, at least 1400 protein groups, at least 1500protein groups, at least 1600 protein groups, at least 1700 proteingroups, at least 1800 protein groups, at least 1900 protein groups, atleast 2000 protein groups, at least 2100 protein groups, at least 2200protein groups, at least 2300 protein groups, at least 2400 proteingroups, at least 2500 protein groups, at least 2600 protein groups, atleast 2700 protein groups, at least 2800 protein groups, at least 2900protein groups, at least 3000 protein groups, at least 3100 proteingroups, at least 3200 protein groups, at least 3300 protein groups, atleast 3400 protein groups, at least 3500 protein groups, at least 3600protein groups, at least 3700 protein groups, at least 3800 proteingroups, at least 3900 protein groups, at least 4000 protein groups, atleast 4100 protein groups, at least 4200 protein groups, at least 4300protein groups, at least 4400 protein groups, at least 4500 proteingroups, at least 4600 protein groups, at least 4700 protein groups, atleast 4800 protein groups, at least 4900 protein groups, at least 5000protein groups, at least 10000 protein groups, at least 20000 proteingroups, at least 100000 protein groups, from 100 to 5000 protein groups,from 200 to 4700 protein groups, from 300 to 4400 protein groups, from400 to 4100 protein groups, from 500 to 3800 protein groups, from 600 to3500 protein groups, from 700 to 3200 protein groups, from 800 to 2900protein groups, from 900 to 2600 protein groups, from 1000 to 2300protein groups, from 1000 to 3000 protein groups, from 3000 to 4000protein groups, from 4000 to 5000 protein groups, from 5000 to 6000protein groups, from 6000 to 7000 protein groups, from 7000 to 8000protein groups, from 8000 to 9000 protein groups, from 9000 to 10000protein groups, from 10000 to 11000 protein groups, from 11000 to 12000protein groups, from 12000 to 13000 protein groups, from 13000 to 14000protein groups, from 14000 to 15000 protein groups, from 15000 to 16000protein groups, from 16000 to 17000 protein groups, from 17000 to 18000protein groups, from 18000 to 19000 protein groups, from 19000 to 20000protein groups, from 20000 to 25000 protein groups, from 25000 to 30000protein groups, from 10000 to 20000 protein groups, from 10000 to 50000protein groups, from 20000 to 100000 protein groups, from 2000 to 20000protein groups, from 1800 to 20000 protein groups, or from 10000 to100000 protein groups.

The automated apparatuses, systems, methods, and sensor elements (e.g.,particles) disclosed herein can be used to identify at least at least100 proteins, at least 200 proteins, at least 300 proteins, at least 400proteins, at least 500 proteins, at least 600 proteins, at least 700proteins, at least 800 proteins, at least 900 proteins, at least 1000proteins, at least 1100 proteins, at least 1200 proteins, at least 1300proteins, at least 1400 proteins, at least 1500 proteins, at least 1600proteins, at least 1700 proteins, at least 1800 proteins, at least 1900proteins, at least 2000 proteins, at least 2100 proteins, at least 2200proteins, at least 2300 proteins, at least 2400 proteins, at least 2500proteins, at least 2600 proteins, at least 2700 proteins, at least 2800proteins, at least 2900 proteins, at least 3000 proteins, at least 3100proteins, at least 3200 proteins, at least 3300 proteins, at least 3400proteins, at least 3500 proteins, at least 3600 proteins, at least 3700proteins, at least 3800 proteins, at least 3900 proteins, at least 4000proteins, at least 4100 proteins, at least 4200 proteins, at least 4300proteins, at least 4400 proteins, at least 4500 proteins, at least 4600proteins, at least 4700 proteins, at least 4800 proteins, at least 4900proteins, at least 5000 proteins, from 100 to 5000 proteins, from 200 to4700 proteins, from 300 to 4400 proteins, from 400 to 4100 proteins,from 500 to 3800 proteins, from 600 to 3500 proteins, from 700 to 3200proteins, from 800 to 2900 proteins, from 900 to 2600 proteins, from1000 to 2300 proteins, from 1000 to 3000 proteins, from 3000 to 4000proteins, from 4000 to 5000 proteins, from 5000 to 6000 proteins, from6000 to 7000 proteins, from 7000 to 8000 proteins, from 8000 to 9000proteins, from 9000 to 10000 proteins, from 10000 to 11000 proteins,from 11000 to 12000 proteins, from 12000 to 13000 proteins, from 13000to 14000 proteins, from 14000 to 15000 proteins, from 15000 to 16000proteins, from 16000 to 17000 proteins, from 17000 to 18000 proteins,from 18000 to 19000 proteins, from 19000 to 20000 proteins, from 20000to 25000 proteins, from 25000 to 30000 proteins, or from 10000 to 20000proteins.

The sensor elements disclosed herein can be used to identify the numberof distinct proteins disclosed herein, and/or any of the specificproteins disclosed herein, over a wide dynamic range. For example, aplurality of particles disclosed herein comprising distinct particletypes, can enrich for proteins in a sample, which can be identifiedusing the methods of the present disclosure, over the entire dynamicrange at which proteins are present in a sample (e.g., a plasma sample).A particle panel may include any number of distinct particle typesdisclosed herein, and may enrich and identify biomolecules over aconcentration range of at least 2 to at least 12 orders of magnitude ina sample.

Disease Detection

The systems and methods described herein can be used for detection ofmarkers in a sample from a subject, which are consistent with aparticular biological (e.g., disease) state. The biological state may bea disease, disorder, or tissue abnormality. The disease state may be anearly phase or intermediate phase disease state.

The systems and methods of the present disclosure can be used to detecta wide range of disease states in a given sample. For example, thesystems and methods of the present disclosure can be used to detect acancer. The cancer may be brain cancer, lung cancer, pancreatic cancer,glioblastoma, meningioma, myeloma, or pancreatic cancer.

In some cases, a biomolecule fingerprint can be used to determine thedisease state of a subject, diagnose or prognose a disease in a subjector identify unique patterns of biomarkers that are associated with adisease state or a disease or disorder. For example, the changes in thebiomolecule fingerprint in a subject over time (days, months, years)allows for the ability to track a disease or disorder in a subject (e.g.disease state) which may be broadly applicable to determination of abiomolecule fingerprint that can be associated with the early stage of adisease or any other disease state. As disclosed herein, the ability todetect a disease early on, for example cancer, even before it fullydevelops or metastasizes allows for a significant increase in positiveoutcomes for those patients and the ability to increase life expectancyand lower mortality associated with that disease.

The automated apparatuses, systems, methods, and sensor elements (e.g.,particles) disclosed herein can provide a unique opportunity to be ableto develop biomolecule fingerprints associated with the pre-stages orprecursor states of the disease in a high-throughput fashion. Thepresent disclosure provides for large scale, fast processing of samplesto generate biomolecule fingerprints in a high throughput manner,thereby allowing for large scale determination of disease state of asubject, diagnosis or prognosis a disease in a subject or identificationof unique patterns of biomarkers that are associated with a diseasestate or a disease or disorder, across many subjects.

In some embodiments, a method of detecting a disease or disorder in asubject are provided. The method comprises the steps of (a) obtaining asample from the subject; (b) contacting the sample with a sensor arrayas described herein, and (c) determining a biomolecule fingerprintassociated with the sample, wherein the biomolecule fingerprintdifferentiates the health of subject in a disease state, for example,from no disease or disorder, having a precursor of a disease ordisorder, and having disease or disorder.

Determining whether a biomolecule fingerprint associated with the samplemay comprise detecting the biomolecule corona signature for at least twosensor elements, wherein the combination of the at least two biomoleculecorona signatures produces the biomolecule fingerprint. In someembodiments, the biomolecule corona signatures of the at least twosensor elements are assayed separately, and the results combined todetermine the biomolecule fingerprint. In some embodiments thebiomolecule corona signatures of the at least two elements are assayedat the same time or in the same sample.

The automated apparatuses, systems, sensor arrays, and methods describedherein can be used to determine a disease state, and/or prognose ordiagnose a disease or disorder. The diseases or disorders contemplatedinclude, but are not limited to, for example, cancer, cardiovasculardisease, endocrine disease, inflammatory disease, a neurological diseaseand the like.

In one embodiment, the disease or disorder is cancer. The term “cancer”is meant to encompass any cancer, neoplastic and preneoplastic diseasethat is characterized by abnormal growth of cells, including tumors andbenign growths. Cancer may, for example, be lung cancer, pancreaticcancer, or skin cancer. In suitable embodiments, the automatedapparatuses, systems, sensor arrays, and methods described herein arenot only able to diagnose cancer (e.g. determine if a subject (a) doesnot have cancer, (b) is in a pre-cancer development stage, (c) is inearly stage of cancer, (d) is in a late stage of cancer) but in someembodiments is able to determine the type of cancer. As demonstrated inthe examples below, a sensor array comprising six sensor elements wasable to accurately determine the disease state of the presence orabsence of cancer. Additionally, the Examples demonstrate that a sensorarray comprising six sensor elements was able to distinguish betweendifferent cancer types (e.g. lung cancer, glioblastoma, meningioma,myeloma and pancreatic cancer).

The automated apparatuses, systems, sensor arrays, and methods of thepresent disclosure can additionally be used to detect other cancers,such as acute lymphoblastic leukemia (ALL); acute myeloid leukemia(AML); cancer in adolescents; adrenocortical carcinoma; childhoodadrenocortical carcinoma; unusual cancers of childhood; AIDS-relatedcancers; kaposi sarcoma (soft tissue sarcoma); AIDS-related lymphoma(lymphoma); primary cns lymphoma (lymphoma); anal cancer; appendixcancer—see gastrointestinal carcinoid tumors; astrocytomas, childhood(brain cancer); atypical teratoid/rhabdoid tumor, childhood, centralnervous system (brain cancer); basal cell carcinoma of the skin—see skincancer; bile duct cancer; bladder cancer; childhood bladder cancer; bonecancer (includes ewing sarcoma and osteosarcoma and malignant fibroushistiocytoma); brain tumors; breast cancer; childhood breast cancer;bronchial tumors, childhood; burkitt lymphoma—see non-hodgkin lymphoma;carcinoid tumor (gastrointestinal); childhood carcinoid tumors;carcinoma of unknown primary; childhood carcinoma of unknown primary;cardiac (heart) tumors, childhood; central nervous system; atypicalteratoid/rhabdoid tumor, childhood (brain cancer); embryonal tumors,childhood (brain cancer); germ cell tumor, childhood (brain cancer);primary cns lymphoma; cervical cancer; childhood cervical cancer;childhood cancers; cancers of childhood, unusual; cholangiocarcinoma—seebile duct cancer; chordoma, childhood; chronic lymphocytic leukemia(CLL); chronic myelogenous leukemia (CIVIL); chronic myeloproliferativeneoplasms; colorectal cancer; childhood colorectal cancer;craniopharyngioma, childhood (brain cancer); cutaneous t-celllymphoma—see lymphoma (mycosis fungoides and sézary syndrome); ductalcarcinoma in situ (DCIS)—see breast cancer; embryonal tumors, centralnervous system, childhood (brain cancer); endometrial cancer (uterinecancer); ependymoma, childhood (brain cancer); esophageal cancer;childhood esophageal cancer; esthesioneuroblastoma (head and neckcancer); ewing sarcoma (bone cancer); extracranial germ cell tumor,childhood; extragonadal germ cell tumor; eye cancer; childhoodintraocular melanoma; intraocular melanoma; retinoblastoma; fallopiantube cancer; fibrous histiocytoma of bone, malignant, and osteosarcoma;gallbladder cancer; gastric (stomach) cancer; childhood gastric(stomach) cancer; gastrointestinal carcinoid tumor; gastrointestinalstromal tumors (GIST) (soft tissue sarcoma); childhood gastrointestinalstromal tumors; germ cell tumors; childhood central nervous system germcell tumors (brain cancer); childhood extracranial germ cell tumors;extragonadal germ cell tumors; ovarian germ cell tumors; testicularcancer; gestational trophoblastic disease; hairy cell leukemia; head andneck cancer; heart tumors, childhood; hepatocellular (liver) cancer;histiocytosis, langerhans cell; hodgkin lymphoma; hypopharyngeal cancer(head and neck cancer); intraocular melanoma; childhood intraocularmelanoma; islet cell tumors, pancreatic neuroendocrine tumors; kaposisarcoma (soft tissue sarcoma); kidney (renal cell) cancer; langerhanscell histiocytosis; laryngeal cancer (head and neck cancer); leukemia;lip and oral cavity cancer (head and neck cancer); liver cancer; lungcancer (non-small cell and small cell); childhood lung cancer; lymphoma;male breast cancer; malignant fibrous histiocytoma of bone andosteosarcoma; melanoma; childhood melanoma; melanoma, intraocular (eye);childhood intraocular melanoma; merkel cell carcinoma (skin cancer);mesothelioma, malignant; childhood mesothelioma; metastatic cancer;metastatic squamous neck cancer with occult primary (head and neckcancer); midline tract carcinoma with nut gene changes; mouth cancer(head and neck cancer); multiple endocrine neoplasia syndromes; multiplemyeloma/plasma cell neoplasms; mycosis fungoides (lymphoma);myelodysplastic syndromes, myelodysplastic/myeloproliferative neoplasms;myelogenous leukemia, chronic (cml); myeloid leukemia, acute (aml);myeloproliferative neoplasms, chronic; nasal cavity and paranasal sinuscancer (head and neck cancer); nasopharyngeal cancer (head and neckcancer); neuroblastoma; non-hodgkin lymphoma; non-small cell lungcancer; oral cancer, lip and oral cavity cancer and oropharyngeal cancer(head and neck cancer); osteosarcoma and malignant fibrous histiocytomaof bone; ovarian cancer; childhood ovarian cancer; pancreatic cancer;childhood pancreatic cancer; pancreatic neuroendocrine tumors (isletcell tumors); papillomatosis (childhood laryngeal); paraganglioma;childhood paraganglioma; paranasal sinus and nasal cavity cancer (headand neck cancer); parathyroid cancer; penile cancer; pharyngeal cancer(head and neck cancer); pheochromocytoma; childhood pheochromocytoma;pituitary tumor; plasma cell neoplasm/multiple myeloma; pleuropulmonaryblastoma; pregnancy and breast cancer; primary central nervous system(CNS) lymphoma; primary peritoneal cancer; prostate cancer; rectalcancer; recurrent cancer; renal cell (kidney) cancer; retinoblastoma;rhabdomyosarcoma, childhood (soft tissue sarcoma); salivary gland cancer(head and neck cancer); sarcoma; childhood rhabdomyosarcoma (soft tissuesarcoma); childhood vascular tumors (soft tissue sarcoma); ewing sarcoma(bone cancer); kaposi sarcoma (soft tissue sarcoma); osteosarcoma (bonecancer); soft tissue sarcoma; uterine sarcoma; sézary syndrome(lymphoma); skin cancer; childhood skin cancer; small cell lung cancer;small intestine cancer; soft tissue sarcoma; squamous cell carcinoma ofthe skin—see skin cancer; squamous neck cancer with occult primary,metastatic (head and neck cancer); stomach (gastric) cancer; childhoodstomach (gastric) cancer; t-cell lymphoma, cutaneous—see lymphoma(mycosis fungoides and sézary syndrome); testicular cancer; childhoodtesticular cancer; throat cancer (head and neck cancer); nasopharyngealcancer; oropharyngeal cancer; hypopharyngeal cancer; thymoma and thymiccarcinoma; thyroid cancer; transitional cell cancer of the renal pelvisand ureter (kidney (renal cell) cancer); carcinoma of unknown primary;childhood cancer of unknown primary; unusual cancers of childhood;ureter and renal pelvis, transitional cell cancer (kidney (renal cell)cancer; urethral cancer; uterine cancer, endometrial; uterine sarcoma;vaginal cancer; childhood vaginal cancer; vascular tumors (soft tissuesarcoma); vulvar cancer; wilms tumor and other childhood kidney tumors;or cancer in young adults.

In some cases, the disease or disorder is cardiovascular disease. Asused herein, the terms “cardiovascular disease” (CVD) or “cardiovasculardisorder” are used to classify numerous conditions affecting the heart,heart valves, and vasculature (e.g., veins and arteries) of the body andencompasses diseases and conditions including, but not limited toatherosclerosis, myocardial infarction, acute coronary syndrome, angina,congestive heart failure, aortic aneurysm, aortic dissection, iliac orfemoral aneurysm, pulmonary embolism, atrial fibrillation, stroke,transient ischemic attack, systolic dysfunction, diastolic dysfunction,myocarditis, atrial tachycardia, ventricular fibrillation, endocarditis,peripheral vascular disease, and coronary artery disease (CAD). Further,the term cardiovascular disease refers to subjects that ultimately havea cardiovascular event or cardiovascular complication, referring to themanifestation of an adverse condition in a subject brought on bycardiovascular disease, such as sudden cardiac death or acute coronarysyndrome, including, but not limited to, myocardial infarction, unstableangina, aneurysm, stroke, heart failure, non-fatal myocardialinfarction, stroke, angina pectoris, transient ischemic attacks, aorticaneurysm, aortic dissection, cardiomyopathy, abnormal cardiaccatheterization, abnormal cardiac imaging, stent or graftrevascularization, risk of experiencing an abnormal stress test, risk ofexperiencing abnormal myocardial perfusion, and death.

As used herein, the ability to detect, diagnose or prognosecardiovascular disease, for example, atherosclerosis, can includedetermining if the patient is in a pre-stage of cardiovascular disease,has developed early, moderate or severe forms of cardiovascular disease,or has suffered one or more cardiovascular event or complicationassociated with cardiovascular disease.

Atherosclerosis (also known as arteriosclerotic vascular disease orASVD) is a cardiovascular disease in which an artery-wall thickens as aresult of invasion and accumulation and deposition of arterial plaquescontaining white blood cells on the innermost layer of the walls ofarteries resulting in the narrowing and hardening of the arteries. Thearterial plaque is an accumulation of macrophage cells or debris, andcontains lipids (cholesterol and fatty acids), calcium and a variableamount of fibrous connective tissue. Diseases associated withatherosclerosis include, but are not limited to, atherothrombosis,coronary heart disease, deep venous thrombosis, carotid artery disease,angina pectoris, peripheral arterial disease, chronic kidney disease,acute coronary syndrome, vascular stenosis, myocardial infarction,aneurysm or stroke. In one embodiment the automated apparatuses,compositions, and methods of the present disclosure may distinguish thedifferent stages of atherosclerosis, including, but not limited to, thedifferent degrees of stenosis in a subject.

In some cases, the disease or disorder is an endocrine disease. The term“endocrine disease” is used to refer to a disorder associated withdysregulation of endocrine system of a subject. Endocrine diseases mayresult from a gland producing too much or too little of an endocrinehormone causing a hormonal imbalance, or due to the development oflesions (such as nodules or tumors) in the endocrine system, which mayor may not affect hormone levels. Suitable endocrine diseases able to betreated include, but are not limited to, e.g., Acromegaly, Addison'sDisease, Adrenal Cancer, Adrenal Disorders, Anaplastic Thyroid Cancer,Cushing's Syndrome, De Quervain's Thyroiditis, Diabetes, FollicularThyroid Cancer, Gestational Diabetes, Goiters, Graves' Disease, GrowthDisorders, Growth Hormone Deficiency, Hashimoto's Thyroiditis, HurthleCell Thyroid Cancer, Hyperglycemia, Hyperparathyroidism,Hyperthyroidism, Hypoglycemia, Hypoparathyroidism, Hypothyroidism, LowTestosterone, Medullary Thyroid Cancer, MEN 1, MEN 2A, MEN 2B,Menopause, Metabolic Syndrome, Obesity, Osteoporosis, Papillary ThyroidCancer, Parathyroid Diseases, Pheochromocytoma, Pituitary Disorders,Pituitary Tumors, Polycystic Ovary Syndrome, Prediabetes, Silent,Thyroiditis, Thyroid Cancer, Thyroid Diseases, Thyroid Nodules,Thyroiditis, Turner Syndrome, Type 1 Diabetes, Type 2 Diabetes, and thelike.

In some cases, the disease or disorder is an inflammatory disease. Asreferred to herein, inflammatory disease refers to a disease caused byuncontrolled inflammation in the body of a subject. Inflammation is abiological response of the subject to a harmful stimulus which may beexternal or internal such as pathogens, necrosed cells and tissues,irritants etc. However, when the inflammatory response becomes abnormal,it results in self-tissue injury and may lead to various diseases anddisorders. Inflammatory diseases can include, but are not limited to,asthma, glomerulonephritis, inflammatory bowel disease, rheumatoidarthritis, hypersensitivities, pelvic inflammatory disease, autoimmunediseases, arthritis; necrotizing enterocolitis (NEC), gastroenteritis,pelvic inflammatory disease (PID), emphysema, pleurisy, pyelitis,pharyngitis, angina, acne vulgaris, urinary tract infection,appendicitis, bursitis, colitis, cystitis, dermatitis, phlebitis,rhinitis, tendonitis, tonsillitis, vasculitis, autoimmune diseases;celiac disease; chronic prostatitis, hypersensitivities, reperfusioninjury; sarcoidosis, transplant rejection, vasculitis, interstitialcystitis, hay fever, periodontitis, atherosclerosis, psoriasis,ankylosing spondylitis, juvenile idiopathic arthritis, Behcet's disease,spondyloarthritis, uveitis, systemic lupus erythematosus, and cancer.For example, the arthritis includes rheumatoid arthritis, psoriaticarthritis, osteoarthritis or juvenile idiopathic arthritis, and thelike.

The disease or disorder may be a neurological disease. Neurologicaldisorders or neurological diseases are used interchangeably and refer todiseases of the brain, spine and the nerves that connect them.Neurological diseases include, but are not limited to, brain tumors,epilepsy, Parkinson's disease, Alzheimer's disease, ALS, arteriovenousmalformation, cerebrovascular disease, brain aneurysms, epilepsy,multiple sclerosis, Peripheral Neuropathy, Post-Herpetic Neuralgia,stroke, frontotemporal dementia, demyelinating disease (including butare not limited to, multiple sclerosis, Devic's disease (i.e.neuromyelitis optica), central pontine myelinolysis, progressivemultifocal leukoencephalopathy, leukodystrophies, Guillain-Barresyndrome, progressing inflammatory neuropathy, Charcot-Marie-Toothdisease, chronic inflammatory demyelinating polyneuropathy, and anti-MAGperipheral neuropathy) and the like. Neurological disorders also includeimmune-mediated neurological disorders (IMNDs), which include diseaseswith at least one component of the immune system reacts against hostproteins present in the central or peripheral nervous system andcontributes to disease pathology. IMNDs may include, but are not limitedto, demyelinating disease, paraneoplastic neurological syndromes,immune-mediated encephalomyelitis, immune-mediated autonomic neuropathy,myasthenia gravis, autoantibody-associated encephalopathy, and acutedisseminated encephalomyelitis.

Methods, systems, and/or apparatuses of the present disclosure may beable to accurately distinguish between patients with or withoutAlzheimer's disease. These may also be able to detect patients who arepre-symptomatic and may develop Alzheimer's disease several years afterthe screening. This provides advantages of being able to treat a diseaseat a very early stage, even before development of the disease.

The systems, methods, and apparatuses of the present disclosure candetect a pre-disease stage of a disease or disorder. A pre-disease stageis a stage at which the patient has not developed any signs or symptomsof the disease. A pre-cancerous stage would be a stage in which canceror tumor or cancerous cells have not be identified within the subject. Apre-neurological disease stage would be a stage in which a person hasnot developed one or more symptom of the neurological disease. Theability to diagnose a disease before one or more sign or symptom of thedisease is present allows for close monitoring of the subject and theability to treat the disease at a very early stage, increasing theprospect of being able to halt progression or reduce the severity of thedisease.

The automated apparatuses, systems, sensor arrays, and methods of thepresent disclosure in some embodiments are able to detect the earlystages of a disease or disorder. Early stages of the disease can referto when the first signs or symptoms of a disease may manifest within asubject. The early stage of a disease may be a stage at which there areno outward signs or symptoms. For example, in Alzheimer's disease anearly stage may be a pre-Alzheimer's stage in which no symptoms aredetected yet the patient will develop Alzheimer's months or years later.

Identifying a disease in either pre-disease development or in the earlystates may often lead to a higher likelihood for a positive outcome forthe patient. For example, diagnosing cancer at an early stage (stage 0or stage 1) can increase the likelihood of survival by over 80%. Stage 0cancer can describe a cancer before it has begun to spread to nearbytissues. This stage of cancer is often highly curable, usually byremoving the entire tumor with surgery. Stage 1 cancer may usually be asmall cancer or tumor that has not grown deeply into nearby tissue andhas not spread to lymph nodes or other parts of the body.

FIG. 8 presents a schematic overview of a cancer detection method thatcan be performed using the automated apparatus of the presentdisclosure. Whole blood samples can be collected from a range ofpatients, including healthy patients and patients with different typesand stages of cancer. The whole blood can be fractionated into plasmasamples, and then contacted with a plurality of types of particles,including positively charged, negatively charged, and neutral particles.Each particle type collects different types of proteins from the plasmasamples, leading to each patient having a unique biomoleculefingerprint. The biomolecule fingerprint not only comprise the relativeabundances of proteins on each particle type, but also the relativeabundances of proteins across particle types. For example, an increasein the abundance of fibronectin on a first particle type may be arelevant indicator only when the abundance of complement component 4 islow on a second particle type. The biomolecule fingerprints can not onlybe used to determine which patients have cancer, but also to determinethe stages and types of the cancers.

In some embodiments, the automated apparatuses, systems, sensor arrays,and methods are able to detect intermediate stages of the disease.Intermediate states of the disease describe stages of the disease thathave passed the first signs and symptoms and the patient is experiencingone or more symptom of the disease. For example, for cancer, stage II orIII cancers are considered intermediate stages, indicating largercancers or tumors that have grown more deeply into nearby tissue. Insome instances, stage II or III cancers may have also spread to lymphnodes but not to other parts of the body.

Further, the automated apparatuses, systems, sensor arrays, and methodsare able to detect late or advanced stages of the disease. Late oradvanced stages of the disease may also be called “severe” or “advanced”and usually indicates that the subject is suffering from multiplesymptoms and effects of the disease. For example, severe stage cancerincludes stage IV, where the cancer has spread to other organs or partsof the body and is sometimes referred to as advanced or metastaticcancer.

The methods of the present disclosure can include processing thebiomolecule fingerprint of the sample against a collection ofbiomolecule fingerprints associated with a plurality of diseases and/ora plurality of disease states to determine if the sample indicates adisease and/or disease state. For example, samples can be collected froma population of subjects over time. Once the subjects develop a diseaseor disorder, the present disclosure allows for the ability tocharacterize and detect the changes in biomolecule fingerprints overtime in the subject by computationally analyzing the biomoleculefingerprint of the sample from the same subject before they havedeveloped a disease to the biomolecule fingerprint of the subject afterthey have developed the disease. Samples can also be taken from cohortsof patients who all develop the same disease, allowing for analysis andcharacterization of the biomolecule fingerprints that are associatedwith the different stages of the disease for these patients (e.g. frompre-disease to disease states).

In some cases, the apparatuses, systems, compositions, and methods ofthe present disclosure are able to distinguish not only betweendifferent types of diseases, but also between the different stages ofthe disease (e.g. early stages of cancer). This can comprisedistinguishing healthy subjects from pre-disease state subjects. Thepre-disease state may be stage 0 or stage 1 cancer, a neurodegenerativedisease, dementia, a coronary disease, a kidney disease, acardiovascular disease (e.g., coronary artery disease), diabetes, or aliver disease. Distinguishing between different stages of the diseasecan comprise distinguishing between two stages of a cancer (e.g., stage0 vs stage 1 or stage 1 vs stage 3).

Sample

The panels of the present disclosure can be used to generate proteomicdata from protein coronas and subsequently associated with any of thebiological states described herein. Samples consistent with the presentdisclosure include biological samples from a subject. The subject may bea human or a non-human animal. Biological samples may be a biofluid. Forexample, the biofluid may be plasma, serum, CSF, urine, tear, or saliva.Said biological samples can contain a plurality of proteins or proteomicdata, which may be analyzed after adsorption of proteins to the surfaceof the various sensor element (e.g., particle) types in a panel andsubsequent digestion of protein coronas. Proteomic data can comprisenucleic acids, peptides, or proteins.

A wide range of biological samples are compatible for use within theautomated apparatuses of the present disclosure. The biological samplemay comprise plasma, serum, urine, cerebrospinal fluid, synovial fluid,tears, saliva, whole blood, milk, nipple aspirate, ductal lavage,vaginal fluid, nasal fluid, ear fluid, gastric fluid, pancreatic fluid,trabecular fluid, lung lavage, sweat, crevicular fluid, semen, prostaticfluid, sputum, fecal matter, bronchial lavage, fluid from swabbings,bronchial aspirants, fluidized solids, fine needle aspiration samples,tissue homogenates, lymphatic fluid, cell culture samples, or anycombination thereof. The biological sample may comprise multiplebiological samples (e.g., pooled plasma from multiple subjects, ormultiple tissue samples from a single subject). The biological samplemay comprise a single type of biofluid or biomaterial from a singlesource.

The biological sample may be diluted or pre-treated. The biologicalsample may undergo depletion (e.g., the biological sample comprisesserum) prior to use within the automated apparatus. The biologicalsample may also undergo physical (e.g., homogenization or sonication) orchemical treatment prior to use within the automated apparatus. Thebiological sample may be diluted prior to use within the automatedapparatus. The dilution medium may comprise buffer or salts, or bepurified water (e.g., distilled water). Different partitions of abiological sample may undergo different degrees of dilution. Abiological sample or sample partition may undergo a 1.1-fold, 1.2-fold,1.3-fold, 1.4-fold, 1.5-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold,8-fold, 10-fold, 12-fold, 15-fold, 20-fold, 30-fold, 40-fold, 50-fold,75-fold, 100-fold, 200-fold, 500-fold, or 1000-fold dilution.

In some embodiments, the panels of the present disclosure provideidentification and measurement of particular proteins in the biologicalsamples by processing of the proteomic data via digestion of coronasformed on sensor elements. Examples of proteins that can be identifiedand measured include highly abundant proteins, proteins of mediumabundance, and low-abundance proteins. Examples of proteins that arehighly abundant proteins include albumin and IgG.

In some embodiments, examples of proteins that can be measured andidentified include albumin, immunoglobulin G (IgG), lysozyme, carcinoembryonic antigen (CEA), receptor tyrosine-protein kinase erbB-2(HER-2/neu), bladder tumor antigen, thyroglobulin, alpha-fetoprotein,prostate specific antigen (PSA), mucin 16 (CA125), carbohydrate antigen19-9 (CA19.9), carcinoma antigen 15-3 (CA15.3), leptin, prolactin,osteopontin, insulin-like growth factor 2 (IGF-II), 4F2 cell-surfaceantigen heavy chain (CD98), fascin, sPigR, 14-3-3 eta, troponin I,B-type natriuretic peptide, breast cancer type 1 susceptibility protein(BRCA1), c-Myc proto-oncogene protein (c-Myc), interleukin-6 (IL-6),fibrinogen. Epidermal growth factor receptor (EGFR), gastrin, PH,Granulocyte colony-stimulating factor (G CSF), desmin, enolase 1 (NSE),folice-stimulating hormone (FSH), vascular endothelial growth factor(VEGF), P21, Proliferating cell nuclear antigen (PCNA), calcitonin,pathogenesis-related proteins (PR), luteinizing hormone (LH),somatostatin. S100, insulin. alpha-prolactin, Adrenocorticotropichormone (ACTH), B-cell lymphoma 2 (Bcl 2), estrogen receptor alpha (ERalpha), antigen k (Ki-67), tumor protein (p53), cathepsin D, betacatenin, von Willebrand factor (VWF), CD15, k-ras, caspase 3, ENTHdomain-containing protein (EPN), CD10, FAS, breast cancer type 2susceptibility protein (BRCA2), CD30L, CD30, CGA, CRP, prothrombin,CD44, APEX, transferrin, GM-CSF, E-cadherin, interleukin-2 (IL-2), Bax,IFN-gamma, beta-2-MG, tumor necrosis factor alpha (TNF alpha), clusterof differentiation 340, trypsin, cyclin D1, MG B, XBP-1, HG-1, YKL-40,S-gamma, NESP-55, netrin-1, geminin, GADD45A, CDK-6, CCL21, breastcancer metastasis suppressor 1 (BrMS1), 17betaHDI, platelet-derivedgrowth factor receptor A (PDGRFA), P300/CBP-associated factor (Pcaf),chemokine ligand 5 (CCLS), matrix metalloproteinase-3 (MMP3), claudin-4,and claudin-3.

Methods of Analysis

The proteomic data of the sample can be identified, measured, andquantified using a number of different analytical techniques. Forexample, proteomic data can be analyzed using sodium dodecyl sulfatepolyacrylamide gel electrophoresis (SDS-PAGE) or any gel-basedseparation technique. Peptides and proteins can also be identified,measured, and quantified using an immunoassay, such as an enzyme-linkedimmunosorbent assay (ELISA). Alternatively, proteomic data can beidentified, measured, and quantified using mass spectrometry, highperformance liquid chromatography, LC-MS/MS, and other proteinseparation techniques.

In some cases, the method of determining the biomolecule fingerprintcomprises detecting and determining the biomolecular corona signaturesof the at least two sensor elements. This step can be done by separatingthe plurality of biomolecules attached to each sensor element (e.g.separating the biomolecule corona from the sensor element) and assayingthe plurality of biomolecules to determine the composition of theplurality of biomolecule coronas to determine a biomolecule fingerprint.In some cases, the composition of each biomolecule corona signature ofeach sensor element is assayed independently, and the results arecombined to produce the biomolecule fingerprint (e.g. each sensorelement is in a separate channel or compartment wherein the specificcomposition of the biomolecule corona for that specific sensor elementcan be separately analyzed (e.g. either by detaching the biomoleculesand assaying by mass spectrometry and/or chromatography or by detectingthe plurality of biomolecules still attached to the sensor element byfluorescence, luminescence or other means). The at least two sensorelements may also be in the same partition and the composition of thebiomolecule corona for the at least two sensor elements is assayed atthe same time by dissociating the biomolecule corona from both sensorelements into one solution and assaying that solution to determining abiomolecule signature.

Methods of assaying the plurality of biomolecules that make up thebiomolecule corona signature or the biomolecule fingerprint may include,but are not limited to, for example, gel-electrophoresis, liquidchromatography, mass spectrometry, nuclear magnetic resonancespectroscopy (NMR), Fourier transform infrared spectroscopy (FTIR),circular dichroism, Raman spectrometry, and a combination thereof. Insome cases, the assaying comprises an analyte specific identificationtechnique, such as ELISA, immunostaining, or nucleic acid capture byhybridization. In a preferred embodiment, the assaying comprises liquidchromatography, mass spectrometry or a combination thereof.

As disclosed herein, nucleic acids may be processed by standardmolecular biology techniques for downstream applications. Embodiments ofthe methods and compositions disclosed herein relate to nucleic acid(polynucleotide) sequencing. In some methods and compositions describedherein, the nucleotide sequence of a portion of a target nucleic acid orfragment thereof may be determined using a variety of methods anddevices. Examples of sequencing methods include electrophoretic,sequencing by synthesis, sequencing by ligation, sequencing byhybridization, single-molecule sequencing, and real time sequencingmethods. In some embodiments, the process to determine the nucleotidesequence of a target nucleic acid or fragment thereof may be anautomated process. In some embodiments, capture probes may function asprimers permitting the priming of a nucleotide synthesis reaction usinga polynucleotide from the nucleic acid sample as a template. In thisway, information regarding the sequence of the polynucleotides suppliedto the array may be obtained. In some embodiments, polynucleotideshybridized to capture probes on the array may serve as sequencingtemplates if primers that hybridize to the polynucleotides bound to thecapture probes and sequencing reagents are further supplied to thearray. Methods of sequencing using arrays have been described previouslyin the art.

In some embodiments involving sequencing on a substrate such as anarray, paired end reads may be obtained on nucleic acid clusters.Methods for obtaining paired end reads are described in WO/07010252 andWO/07091077, each of which is incorporated herein by reference in itsentirety. Paired end sequencing facilitates reading both the forward andreverse template strands of each cluster during one paired-end read.Generally, template clusters may be amplified on the surface of asubstrate (e.g. a flow-cell) by bridge amplification and sequenced bypaired primers sequentially. Upon amplification of the template strands,a bridged double stranded structure may be produced. This may be treatedto release a portion of one of the strands of each duplex from thesurface. The single stranded nucleic acid may be available forsequencing, primer hybridization and cycles of primer extension. Afterthe first sequencing run, the ends of the first single stranded templatemay be hybridized to the immobilized primers remaining from the initialcluster amplification procedure. The immobilized primers may be extendedusing the hybridized first single strand as a template to resynthesizethe original double stranded structure. The double stranded structuremay be treated to remove at least a portion of the first template strandto leave the resynthesized strand immobilized in single stranded form.The resynthesized strand may be sequenced to determine a second read,whose location originates from the opposite end of the original templatefragment obtained from the fragmentation process.

Nucleic acid sequencing may be single-molecule sequencing or sequencingby synthesis. Sequencing may be massively parallel array sequencing(e.g., Illumina™ sequencing), which may be performed using templatenucleic acid molecules immobilized on a support, such as a flow cell.For example, sequencing may comprise a first-generation sequencingmethod, such as Maxam-Gilbert or Sanger sequencing, or a high-throughputsequencing (e.g., next-generation sequencing or NGS) method. Ahigh-throughput sequencing method may sequence simultaneously (orsubstantially simultaneously) at least about 10,000, 100,000, 1 million,10 million, 100 million, 1 billion, or more polynucleotide molecules.Sequencing methods may include, but are not limited to: pyrosequencing,sequencing-by synthesis, single-molecule sequencing, nanoporesequencing, semiconductor sequencing, sequencing-by-ligation,sequencing-by-hybridization, Digital Gene Expression (Helicos),massively parallel sequencing, e.g., Helicos, Clonal Single MoleculeArray (Solexa/Illumina), sequencing using PacBio, SOLiD, Ion Torrent, orNanopore platforms.

A sensor element may comprise a complex with a first component and apolymer fluorophore or other quencher component chemically complementaryto the first component where such a complex having an initial backgroundor reference fluorescence. Once the first component comes into contactwith a biomolecule (e.g., upon formation of a biomolecule corona), itcan affect the quenching of the fluorophore and this change influorescence can be measured. After the sensor is irradiated and/orexcited with a laser, the effect and/or change in fluorescence for eachsensor element can be measured and compared to or processed against thebackground fluorescence to produce the biomolecule fingerprint.

Computer Systems

The present disclosure provides computer control systems that areprogrammed to implement methods of the disclosure. This determination,analysis or statistical classification is done by methods known in theart, including, but not limited to, for example, a wide variety ofsupervised and unsupervised data analysis and clustering approaches suchas hierarchical cluster analysis (HCA), principal component analysis(PCA), Partial least squares Discriminant Analysis (PLSDA), machinelearning (also known as random forest), logistic regression, decisiontrees, support vector machine (SVM), k-nearest neighbors, naive bayes,linear regression, polynomial regression, SVM for regression, K-meansclustering, and hidden Markov models, among others. The computer systemcan perform various aspects of analyzing the protein sets or proteincorona of the present disclosure, such as, for example,comparing/analyzing the biomolecule corona of several samples todetermine with statistical significance what patterns are common betweenthe individual biomolecule coronas to determine a protein set that isassociated with the biological state. The computer system can be used todevelop classifiers to detect and discriminate different protein sets orprotein corona (e.g., characteristic of the composition of a proteincorona). Data collected from the presently disclosed sensor array can beused to train a machine learning algorithm, specifically an algorithmthat receives array measurements from a patient and outputs specificbiomolecule corona compositions from each patient. Before training thealgorithm, raw data from the array can be first denoised to reducevariability in individual variables.

Machine learning can be generalized as the ability of a learning machineto perform accurately on new, unseen examples/tasks after havingexperienced a learning data set. Machine learning may include thefollowing concepts and methods. Supervised learning concepts may.include AODE; Artificial neural network, such as Backpropagation,Autoencoders, Hopfield networks, Boltzmann machines, RestrictedBoltzmann Machines, and Spiking neural networks; Bayesian statistics,such as Bayesian network and Bayesian knowledge base; Case-basedreasoning; Gaussian process regression; Gene expression programming;Group method of data handling (GMDH); Inductive logic programming;Instance-based learning; Lazy learning;

Learning Automata; Learning Vector Quantization; Logistic Model Tree;Minimum message length (decision trees, decision graphs, etc.), such asNearest Neighbor Algorithm and Analogical modeling; Probablyapproximately correct learning (PAC) learning; Ripple down rules, aknowledge acquisition methodology; Symbolic machine learning algorithms;Support vector machines; Random Forests; Ensembles of classifiers, suchas Bootstrap aggregating (bagging) and Boosting (meta-algorithm);Ordinal classification; Information fuzzy networks (IFN); ConditionalRandom Field; ANOVA; Linear classifiers, such as Fisher's lineardiscriminant, Linear regression, Logistic regression, Multinomiallogistic regression, Naive Bayes classifier, Perceptron, Support vectormachines; Quadratic classifiers; k-nearest neighbor; Boosting; Decisiontrees, such as C4.5, Random forests, ID3, CART, SLIQ SPRINT; Bayesiannetworks, such as Naive Bayes; and Hidden Markov models. Unsupervisedlearning concepts may include; Expectation-maximization algorithm;Vector Quantization; Generative topographic map; Information bottleneckmethod; Artificial neural network, such as Self-organizing map;Association rule learning, such as, Apriori algorithm, Eclat algorithm,and FPgrowth algorithm; Hierarchical clustering, such as Singlelinkageclustering and Conceptual clustering; Cluster analysis, such as, K-meansalgorithm, Fuzzy clustering, DBSCAN, and OPTICS algorithm; and OutlierDetection, such as Local Outlier Factor. Semi-supervised learningconcepts may include; Generative models; Low-density separation;Graph-based methods; and Co-training.

Reinforcement learning concepts may include; Temporal differencelearning; Q-learning; Learning Automata; and SARSA. Deep learningconcepts may include; Deep belief networks; Deep Boltzmann machines;Deep Convolutional neural networks; Deep Recurrent neural networks; andHierarchical temporal memory. A computer system may be adapted toimplement a method described herein. The system includes a centralcomputer server that is programmed to implement the methods describedherein. The server includes a central processing unit (CPU, also“processor”) which can be a single core processor, a multi coreprocessor, or plurality of processors for parallel processing. Theserver also includes memory (e.g., random access memory, read-onlymemory, flash memory); electronic storage unit (e.g. hard disk);communications interface (e.g., network adaptor) for communicating withone or more other systems; and peripheral devices which may includecache, other memory, data storage, and/or electronic display adaptors.The memory, storage unit, interface, and peripheral devices are incommunication with the processor through a communications bus (solidlines), such as a motherboard. The storage unit can be a data storageunit for storing data. The server is operatively coupled to a computernetwork (“network”) with the aid of the communications interface. Thenetwork can be the Internet, an intranet and/or an extranet, an intranetand/or extranet that is in communication with the Internet, atelecommunication or data network. The network in some cases, with theaid of the server, can implement a peer-to-peer network, which’ mayenable devices coupled to the server to behave as a client or a server.

The storage unit can store files, such as subject reports, and/orcommunications with the data about individuals, or any aspect of dataassociated with the present disclosure.

The computer server can communicate with one or more remote computersystems through the network. The one or more remote computer systems maybe, for example, personal computers, laptops, tablets, telephones, Smartphones, or personal digital assistants.

In some applications the computer system includes a single server. Inother situations, the system includes multiple servers in communicationwith one another through an intranet, extranet and/or the internet.

The server can be adapted to store measurement data or a database asprovided herein, patient information from the subject, such as, forexample, medical history, family history, demographic data and/or otherclinical or personal information of potential relevance to a particularapplication. Such information can be stored on the storage unit or theserver and such data can be transmitted through a network.

Methods as described herein can be implemented by way of machine (orcomputer processor) executable code (or software) stored on anelectronic storage location of the server, such as, for example, on thememory, or electronic storage unit. During use, the code can be executedby the processor. In some cases, the code can be retrieved from thestorage unit and stored on the memory for ready access by the processor.In some situations, the electronic storage unit can be precluded, andmachine-executable instructions are stored on memory.

Alternatively, the code can be executed on a second computer system.

Aspects of the systems and methods provided herein, such as the server,can be embodied in programming. Various aspects of the technology may bethought of as “products” or “articles of manufacture” typically in theform of machine (or processor) executable code and/or associated datathat is carried on or embodied in a type of machine readable medium.Machine-executable code can be stored on an electronic storage unit,such memory (e.g., read-only memory, random-access memory, flash memory)or a hard disk. “Storage” type media can include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer into the computerplatform of an application server. Thus, another type of media that maybear the software elements includes optical, electrical, andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless likes, optical links, or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to non-transitory, tangible “storage” media, terms such ascomputer or machine “readable medium” can refer to any medium thatparticipates in providing instructions to a processor for execution.

The computer systems described herein may comprise computer-executablecode for performing any of the algorithms or algorithms-based methodsdescribed herein. In some applications the algorithms described hereinwill make use of a memory unit that is comprised of at least onedatabase.

Data relating to the present disclosure can be transmitted over anetwork or connections for reception and/or review by a receiver. Thereceiver can be but is not limited to the subject to whom the reportpertains; or to a caregiver thereof, e.g., a health care provider,manager, other health care professional, or other caretaker; a person orentity that performed and/or ordered the analysis. The receiver can alsobe a local or remote system for storing such reports (e.g. servers orother systems of a “cloud computing” architecture). In one embodiment, acomputer-readable medium includes a medium suitable for transmission ofa result of an analysis of a biological sample using the methodsdescribed herein.

Aspects of the systems and methods provided herein can be embodied inprogramming. Various aspects of the technology may be thought of as“products” or “articles of manufacture” typically in the form of machine(or processor) executable code and/or associated data that is carried onor embodied in a type of machine readable medium. Machine executablecode can be stored on an electronic storage unit, such as memory (e.g.,read-only memory, random-access memory, flash memory) or a hard disk.“Storage” type media can include any or all of the tangible memory ofthe computers, processors or the like, or associated modules thereof,such as various semiconductor memories, tape drives, disk drives and thelike, which may provide nontransitory storage at any time for thesoftware programming. All or portions of the software may at times becommunicated through the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer into the computer platform of anapplication server. Thus, another type of media that may bear thesoftware elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

EXAMPLES

The following examples are included to further describe some aspects ofthe present disclosure and should not be used to limit the scope of thedisclosure.

Example 1: Formation of Protein Coronas with Magnetic Nanoparticles andBiofluid with Full Resuspension

This exemplary procedure applies to creating protein coronas manually inbiofluid samples using a panel of magnetic nanoparticles with fullresuspension of nanoparticles. The systems and methods of the presentdisclosure may apply the procedures described herein.

Materials:

The materials used in creating protein coronas is shown in TABLE 2.

TABLE 2 Equipment and reagents used in creating protein coronasEquipment and Reagent Supplier Part number or Model Number Reagent GradeWater TEKNOVA W1210 or equivalent Reagent Grade Water Corning 46-002-LFor equivalent Microplate F-Bottom Greiner 655901 Aluminum Adhesive PlateVWR 29445-080 or equivalent Sealers Microplate Shaker VWR 12620-926 orequivalent Vortexer VWR 33570 or equivalent Analytical Balance MettlerToledo XP205 Single-channel pipettes (100- Rainin L-1000 or equivalent1000 μL) Single-channel pipettes(20- Rainin L-200 or equivalent 200 μL)Multi-channel pipette (100- Rainin E12-1200 or equivalent 1200 μL)Pipette tips (1000 μL) Rainin GPS-L1000 or equivalent Pipette tips(20-200 μL) Rainin GPS-L250 or equivalent 50 mL Reagent Reservoirs VWR82026-355 or equivalent 1x TE pH 7.4 Quality Biological 351-010-131CHAPS Fisher BP571-5 Potassium Chloride (KCl) J. T. Baker 4001-01Corning 1 L bottle Corning 430518 Nalgene Rapid Flow 1000 mL Nalgene567-0010 or 567-0020 0.1 μm or 0.2 μm filter set

Storage and Handling:

The following reagents were stored at room temperature, as shown inTABLE 3:

TABLE 3 Reagents stored at room temperature Reagent Supplier Part Number1x TE pH 7.4 Quality Biological 351-010-131 CHAPS Fisher BP571-5Potassium Chloride (KCl) J. T. Baker 4001-01 Reagent Grade Water TEKNOVAW1210 Reagent Grade Water Corning 46-002-LF TE 150 mM KCl 0.05% CHAPSSeer Inc. SOP003

The following reagents were stored at about 2-8° C., as shown in TABLE4:

TABLE 4 Reagents stored at 2-8° C. Reagent Supplier Part number TE 150mM KCl 0.05% CHAPS Seer Inc. SOP003

Preparation:

Biofluid samples were removed from the freezer and thawed thoroughly.The nanoparticles were sonicated and vortexed about 10 minutes beforeuse. The TE 150 mM KCl 0.05% CHAPS buffer was prepared before beginningthe assay.

TE 150 mM KCl 0.05% CHAPS Buffer Preparation. 11.18 g potassium chlorideand 500 mg CHAPS were added to a Corning 1 L bottle. 998.3 g of 1×TEpH7.4 buffer was added. Using a house vacuum, the buffer was filteredwith a 0.1 μm or 0.2 μm 1000 mL filter set. The buffer can be stored atroom temperature (for about 1 month) or at 2-8° C. (for longer than 1month). The buffer was mixed well before use.

Nanoparticles Preparation. The nanoparticles (aqueous) were diluted inReagent Grade water to appropriate designated concentration. For drypowder nanoparticles, the dry powder nanoparticles were measured out ona scale before adding the appropriate volume of water to create neededconcentration.

Samples Preparation. The samples were removed from the freezer. Thesamples were thawed thoroughly, and the samples were centrifuged at16,000 G for about 2 minutes. The samples were either diluted with TE150 mM KCl 0.05% CHAPS Buffer (1:5) or kept as neat.

FIG. 9 illustrates a sample preparation method consistent with thepresent disclosure. This method comprises 4 steps that generate a subsetof biomolecules from a biological sample and then use the subset ofbiomolecules to generate a biomolecule fingerprint. The first stepcomprises transferring a plasma sample into a plurality of partitions(e.g., wells within a well plate) which comprises a plurality of sensorelements (e.g., magnetic nanoparticles). The sample is incubated withinthe partitions for 1 hour at 37° C. with shaking, thereby generatingbiomolecule coronas on the sensor elements. The plurality of partitionsis then subjected to a magnetic field that is sufficiently strong toimmobilize the sensor elements within the plurality of partitions. Theplurality of partitions are then subjected to three washes (e.g.,sequential addition and removal of a resuspension buffer) to removebiomolecules that did not adsorb to the sensor elements. After the3^(rd) wash, the particles are resuspended in buffer, resulting in thedesorption of a subset of biomolecules from the biomolecule coronas. Thesubset of biomolecules is then subjected to a set of denaturation andchemical treatment steps, including heating to 95° C., reduction andalkylation, protease digestion, and further washes. The subset ofbiomolecules is then submitted for mass spectrometric analysis, whichgenerates a biomolecule fingerprint for the sample.

Procedure:

The reagents and equipment were prepared as described in the previoussection (see “Preparation”). 1000 μL of diluted nanoparticles wereloaded into each well using a multichannel pipette. 1000 μL of dilutedsamples per nanoparticle well were added using a pipette. The wells weremixed by aspiration with a pipette about 10 times. The plate was coveredwith an adhesive plate sealer and incubated for about 1 hours at 37° C.on a plate shaker set to 300 rpm. After the about 1-hour incubation, theadhesive plate sealer was removed, and the plate was placed on a magnetfor about 5 minutes to form a nanoparticle corona pellet at the wellbottom. For washing, the supernatant was removed with a multichannelpipette. About 2004, of TE 150 mM KCl 0.05% CHAPS Buffer was added usinga pipette and fully resuspended the nanoparticles. The solution wasplaced back on the magnet for about 5 minutes. The washing step wasrepeated 3 times. The nanoparticle pellet was resuspended in anappropriate reagent for BCA, gel or trypsin digestion.

Example 2: Trypsin Gold Digest

Materials:

The materials used in the trypsin gold digest is shown in TABLE 5.

TABLE 5 Reagents used in the trypsin gold digest Reagent Supplier PartNumber Seppro Ammonium Bicarbonate Sigma 52454-200 mL Urea FisherBP169-500 DL-Dithiothreitol (DTT) Sigma 4381-5G Iodoacetamide (IAA)GBiosciences 786-078 Trypsin Gold Promega V5280 Acetic Acid VWRBDH3096-2.5LPC

Preparation:

50 mM ABC (Ammonium Bicarbonate). 0.25 mL of 2M ABC was added to 9.75 mLof water to yield 10 mL of 50 mM ABC. The solution was vortexed andstored at 4° C. for up to a week.

8M Urea. 4.8 g of urea was weighed and 50 mM of ABC was added untilclose to about the 10 mL mark. The solution was vortexed and optionallyswirled in 37° C. incubator to help dissolve. 50 mM ABC was added to the10 mL mark and vortexed.

200 mM DTT. 0.031 g of DTT was weighed and 1 mL 50 mM ABC was added. Thesolution was vortexed and stored away from light at 4° C.

200 mM IAA. 400 uL 50 mM ABC was added to 0.015 g of premeasured IAA.The solution was vortexed and stored at 4° C. The solution was maderight before use.

Trypsin Gold Reconstitution. The solution was prepared as permanufacture PI instructions. 100 uL of 50 mM Acetic Acid was added to100 ug of trypsin, and vortexed. The final concentration was 1 ug/uLtrypsin.

Sample/Trypsin Preparation

40 uL of 8M of urea was added to each sample. The solution was vortexedand sonicated for about 1 min. 2 uL of 200 mM DTT was added to eachsample, and vortexed. The solution was incubated at room temperature forabout 30 min in the dark. 8 uL of 200 mM IAA was added to each sample,and vortexed. The solution was incubated at room temperature for about30 min in the dark. 8 uL of 200 mM DTT was added to each sample, andvortexed. The solution was incubated at room temperature for about 30min in the dark. 50 mM ABC was added so that the added urea was lessthan 2M. 110 uL of 50 mM ABC was added into 58 uL of sample. Theappropriate amount of trypsin was added to the samples. 3 uL ofreconstituted trypsin was added to each tube. The ratio ofprotein:trypsin=˜30 ug protein:1 ug trypsin. The solution was incubatedat 37° C. overnight. 17 uL of 10% FA was added to stop digestion.

Example 3: Proteomic Analysis of NSCLC Samples and Healthy Controls

This example describes proteomic analysis of NSCLC samples and healthycontrols. To demonstrate the utility of the corona analysis platform,the platform's ability was evaluated using a single particle type,poly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA)-coatedSPIONs, and serum samples from 56 subjects (28 with Stage IV NSCLC and28 age- and gender-matched controls) to observe differences between thegroups. The selected subject samples represented a reasonably balancedstudy to identify potential MS features that are different between thegroups. Full data on subject annotation including disease status andco-morbidities are compiled in TABLE 5.

TABLE 6 Gender and age information for the patients from whom the serumsamples were obtained. Age Mean (Standard Class Gender Deviation) NumberHealthy (Control) Female 71.1(7.7) 19 Male  72.4(11.1) 9 Non Small CellLung Female 70.7(7.5) 19 Cancer (Diseased) Male  75.6(13.6) 9

After collection and filtering of the MS1 features followed by log 2transformation of their intensity, the datasets were median scaledwithout respect to class. FIG. 11 shows the normalized intensitydistributions for all 56 subject datasets. All 56 sample MS raw datafiles from the NSCLC versus control study were processed by OpenMSpipeline scripts to extract MS1 features and their intensities andcluster them into feature groups based on overlapping mz and RT valueswithin specified tolerances. Only those feature groups were retainedthat 1) had at least 50% presence of a feature in the group from atleast one of the arms of the comparison and 2) had a feature groupcluster quality above the 25th percentile. The retained features weremedian normalized without respect to class and used for subsequentunivariate analytical comparison. There were no outliers by inspectionof the distributions and all datasets were retained for the univariateanalysis.

There did not appear to be any outlier datasets by inspection.Univariate comparison of feature group intensities between the classeswas performed with a non-parametric, Wilcoxon Test (two-sided). Theresulting p-value for the comparison was corrected for multiple testingusing the method of Benjamin-Hochberg. Using an adjusted p-value cut-offof 0.05, a total of seven feature groups demonstrated statisticalsignificance, as summarized in FIG. 12 .

All five of the proteins identified as differentially abundant betweenthe NSCLC-diseased and control groups have previously been implicated incancer if not actually NSCLC itself. PON1, or paraoxanase-1, has acomplicated pattern in lung cancer including the involvement of arelatively common minor allele variant (Q192R) as a risk factor. At theprotein level, PON1 is modestly decreased in lung adenocarcinoma. SAA1is an acute phase protein that has been shown to be overexpressed inNSCLC in MS-related studies, and the identified peptide was found to beincreased 5.4-fold in diseased subjects. The matrisome factor tenascin C(TENA) has been shown to be increased in primary lung tumors andassociated lymph node metastases compared with normal tissue, and theassociated MS feature was found to be increased by 2-fold in this study.Neural cell adhesion molecule 1 (NCAM1) serves as a marker fordiagnosing lung neuroendocrine tumors. FIBA peptides were identified byMS analysis with increased levels correlating with advancing progressionof lung cancer. Of special note are the two unknown features, Group2 andGroup7, which show differences between control and diseased subjects.Group2 was found in 54 out of 56 subjects and had a modest 33% decreasein diseased subjects. In contrast, Group7 was found only in diseasedsubjects (14 out of 28 members of the class). These results demonstratedthe potential utility for the particle corona to aid in identifyingknown and unknown markers for different disease states.

Example 4: Dynamic Range Compression of Plasma Using Protein CoronaAnalysis

This example describes dynamic range compression using particles tocollect proteins from a plasma sample.

In order to evaluate the ability of particles to compress the measureddynamic range, measured and identified protein feature intensities werecompared to the published values for the concentration of the sameprotein. First, the resulting peptide features for each protein wasselected with the maximum MS-determined intensity of all possiblefeatures for a protein (using the OpenMS MS data processing tools toextract monoisotopic peak values), and then the intensities were modeledagainst the published abundance levels for those same proteins. FIG. 13shows correlation of the maximum intensities of particle corona proteinsand plasma proteins to the published concentration of the same proteins.The blue plotted lines are linear regression models to the data and theshaded regions represent the standard error of the model fit. Thedynamic range of the samples assayed with particles (“S-003,” “S-007,”and “S-011”, detailed in TABLE 1) exhibited a compressed dynamic rangeas compared to the plasma sample not assayed with particles (“Plasma”),as shown by the decrease in slopes of the linear fits. The slopes ofeach plot are 0.47, 0.19, 0.22, and 0.18 for, plasma without particles,plasma with S-003 particles, plasma with S-007 particles, and plasmawith S-011 particles, respectively. FIG. 14 shows the dynamic rangecompression of a protein corona analysis assay with mass spectrometry ascompared to mass spectrometry without particle corona formation. Proteinintensities of common proteins identified in particle coronas in theplasma samples assayed in FIG. 13 (“Nanoparticle MS ln Intensity”) areplotted against the protein intensity identified by mass spectrometry ofplasma without particles (“Plasma MS ln Intensity”). The lightest dottedline shows a slope of 1, indicating the dynamic range of massspectrometry without particles. The slopes of the linear fits to theprotein intensity was 0.12, 0.36, and 0.093 for S-003, S-007, and S-011particles, respectively. The grayed area indicates the standard errorregion of the regression fit.

By comparing the regression model slopes and the intensity span of themeasured data, the biomolecule coronas contain more proteins at lowerabundances (measured or reported) than does plasma. The dynamic range ofthose measured values was compressed (the slope of the regression modelis reduced) for particle measurements as compared to plasmameasurements. This was consistent with previous observations thatparticle can effectively compress the measured dynamic range forabundances in the resulting corona as compared to the original dynamicrange in plasma, which could be attributable to the combination ofabsolute concentration of a protein, its binding affinity to particles,and its interactions with neighboring proteins. The results indicatedthat the biomolecule corona strategy facilitated the identification of abroad spectrum of plasma proteins, particularly those in the lowabundance that are challenging for rapid detection by conventionalproteomic techniques.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

What is claimed is:
 1. An automated system for distinguishing states ofa biological sample using a plurality of particles having surfaces withdifferent physiochemical properties, the system comprising: (a) a fluidtransfer unit comprising a multichannel fluid transfer instrument fortransferring fluids between units within the system; (b) a samplestorage unit comprising a support for storing a plurality of biologicalsamples; (c) a sensing unit comprising a support for a sensor arrayplate possessing partitions that comprise the plurality of particleshaving surfaces with different physiochemical properties for detecting abinding interaction between a population of analytes within thebiological sample and the plurality of particles; (d) a reagent storageunit comprising supports for storing a plurality of reagents; (e) awaste unit comprising supports for storing a reagent to be disposed of;(f) a consumable storage unit comprising supports for storingconsumables for use by the multichannel fluid transfer instrument; and(g) a control unit comprising one or more processors programmed toperform steps comprising: i. contacting the biological sample with aspecified partition of the sensor array; ii. incubating the biologicalsample with the plurality of particles of the sensor array plate; iii.removing components from a partition except the plurality of particlesand a population of analytes interacting with a particle; and iv.preparing the population of analytes for mass spectrometry.
 2. Theautomated system of claim 1, wherein the fluid transfer unit has adegree of mobility that enables access to all other units within thesystem.
 3. The automated system of claim 1, wherein the fluid transferunit has a capacity to perform pipetting functions.
 4. The automatedsystem of claim 1, wherein the support of the sample storage unit orsensing unit comprises a support for a multi-well plate.
 5. Theautomated system of claim 1, wherein the sensor array plate is amulti-well plate.
 6. The automated system of claim 1, wherein the samplestorage unit or the sensing unit comprises a thermal unit capable ofmodulating a temperature of the support and the plurality of biologicalsamples.
 7. The automated system of claim 1, wherein the sample storageunit or the sensing unit comprises a rotational unit capable ofphysically agitating or mixing a sample.
 8. The automated system ofclaim 1, wherein the plurality of particles is immobilized to surfacesof the partitions.
 9. The automated system of claim 1, wherein theplurality of particles comprises a plurality of magnetic nanoparticles.10. The automated system of claim 9, further comprising a magnetizedsupport and a thermal unit capable of modulating the temperature,wherein the one or more processors are further programmed to transferthe sensor array to the magnetized support after (ii).
 11. The automatedsystem of claim 1, wherein the reagent storage unit is configured tosupport a set of reagents for: (a) washing an unbound sample, or (b)preparing a sample for mass spectrometry.
 12. The automated system ofclaim 1, wherein (i) contacting the biological sample within thespecified partition of the sensor array comprises pipetting a specifiedvolume of the biological sample into the specific partition of thesensor array.
 13. The automated system of claim 1, wherein (i)contacting the biological sample with the specified partition of thesensor array comprises pipetting a volume corresponding to at least a1:1, 1:2:1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:15, or 1:20 ratio ofa plurality of particles in a solution to the biological sample.
 14. Theautomated system claim 1, wherein (i) contacting the biological samplewith the specified partition of the sensor array comprises pipetting avolume of at least 10 microliters, at least 50 microliters, at least 100microliters, at least 250 microliters, at least 500 microliters, or atleast 1000 microliters of the biological sample into the specificpartition of the sensor array.
 15. The automated system of claim 1,wherein (ii) incubating the biological sample with the plurality ofparticles contained within the partition of the sensor array platecomprises an incubation time of at least 1 hour.
 16. The automatedsystem of claim 1, wherein (ii) incubating the biological sample withthe plurality of particles contained within the partition of the sensorarray plate comprises an incubation temperature between about 4° C. toabout 40° C.
 17. The automated system of claim 1, wherein removing thecomponents from the partition except the plurality of particles and apopulation of analytes interacting with a particle comprises a series ofwash steps.